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BEGIN:VEVENT
SUMMARY:Tim Hoheisel (McGill University)
DTSTART:20221013T223000Z
DTEND:20221013T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/1
DESCRIPTION:Title: Th
e Maximum Entropy on the Mean Method for Linear Inverse Problems (and beyo
nd)\nby Tim Hoheisel (McGill University) as part of PIMS-CORDS SFU Ope
rations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nThe pr
inciple of ‘maximum entropy’ states that the probability distribution
which best represents the current state of knowledge about a system is the
one with largest entropy with respect to a given prior (data) distributio
n. It was first formulated in the context of statistical physics in two se
minal papers by E. T. Jaynes (Physical Review\, Series II. 1957)\, and thu
s constitutes an information theoretic manifestation of Occam’s razor. W
e bring the idea of maximum entropy to bear in the context of linear inver
se problems in that we solve for the probability measure which is close to
the (learned or chosen) prior and whose expectation has small residual wi
th respect to the observation. Duality leads to tractable\, finite-dimensi
onal (dual) problems. A core tool\, which we then show to be useful beyond
the linear inverse problem setting\, is the ‘MEMM functional’: it is
an infimal projection of the Kullback- Leibler divergence and a linear equ
ation\, which coincides with Cramér’s function (ubiquitous in the theor
y of large deviations) in most cases\, and is paired in duality with the c
umulant generating function of the prior measure. Numerical examples under
line the efficacy of the presented framework.\n
LOCATION:https://researchseminars.org/talk/SFUOR/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nadia Lahrichi (Polytechnique Montreal)
DTSTART:20221027T223000Z
DTEND:20221027T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/2
DESCRIPTION:Title: Im
proving Radiotherapy Treatment Logistics\nby Nadia Lahrichi (Polytechn
ique Montreal) as part of PIMS-CORDS SFU Operations Research Seminar\n\nLe
cture held in ASB 10908.\n\nAbstract\nThe main cancer treatments are surge
ry\, radiation therapy and chemotherapy. The complexity of the logistical
process of scheduling treatment appointments stems from the fact that it i
nvolves extremely costly resources\, sometimes synchronously. Several due
dates (i.e.\, appointments already scheduled\, maximum wait times) and une
xpected events such as the arrival of patients requiring urgent palliative
care add to the difficulty. This talk will investigate how can simulation
and optimization models help improve the efficiency of cancer treatment c
enters and share experiences on patient booking\, physician scheduling\, a
nd capacity assessment. All projects are conducted in close partnership wi
th a hospital and rely on real data.\n
LOCATION:https://researchseminars.org/talk/SFUOR/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Imre Bárány (Rényi Institute and University College London)
DTSTART:20220929T223000Z
DTEND:20220929T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/3
DESCRIPTION:Title: Ce
lls in the Box and a Hyperplane\nby Imre Bárány (Rényi Institute an
d University College London) as part of PIMS-CORDS SFU Operations Research
Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nIt is well known that
a line can intersect at most $2n-1$ cells of the $n \\times n$ chessboard.
What happens in higher dimensions: how many cells of the $d$-dimensional
$[0\,n]^d$ box can a hyperplane intersect? We also prove the integer analo
gue of the following fact. If $K\, L$ are convex bodies in $R^d$ and $K \\
subset L$\, then the surface area $K$ is smaller than that of $L$. Joint w
ork with Peter Frankl.\n
LOCATION:https://researchseminars.org/talk/SFUOR/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jing Lu (University of Winchester)
DTSTART:20221117T233000Z
DTEND:20221118T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/4
DESCRIPTION:Title: Ca
se Studies in Data Science and Analytics from a UK Business School\nby
Jing Lu (University of Winchester) as part of PIMS-CORDS SFU Operations R
esearch Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nData science in
volves the collection\, management\, processing\, analysis\, visualisation
and interpretation of huge amounts of data. It is a multi-disciplinary fi
eld that integrates systematic thinking\, methodology\, process and techno
logy to develop intelligence with respect to real-world problems. This pre
sentation focuses on the business environment and identifies the component
s of data science forming a conceptual architecture before proposing a com
posite data-driven process model. Representative tools and techniques are
applied to relevant case studies demonstrating innovation in undergraduate
programme design\, customer analytics and the marketing of insurance for
example.\n
LOCATION:https://researchseminars.org/talk/SFUOR/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Diego Cifuentes (remote) (Georgia Tech)
DTSTART:20221110T223000Z
DTEND:20221110T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/5
DESCRIPTION:Title: Co
mputing the Nearest Structured Rank Deficient Matrix\nby Diego Cifuent
es (remote) (Georgia Tech) as part of PIMS-CORDS SFU Operations Research S
eminar\n\nLecture held in ASB 10908.\n\nAbstract\nGiven an affine space of
matrices L and a matrix Θ ∈ L\, consider the problem of computing the
closest rank deficient matrix to Θ on L with respect to the Frobenius nor
m. This is a nonconvex problem with several applications in control theory
\, computer algebra\, and computer vision. We introduce a novel semidefini
te programming (SDP) relaxation\, and prove that it always gives the globa
l minimizer of the nonconvex problem in the low noise regime\, i.e.\, when
Θ is close to be rank deficient. Our SDP is the first convex relaxation
for this problem with provable guarantees. We evaluate the performance of
our SDP relaxation in examples from system identification\, approximate GC
D\, triangulation\, and camera resectioning. Our relaxation reliably obtai
ns the global minimizer under non-adversarial noise\, and its noise tolera
nce is significantly better than state of the art methods.\n
LOCATION:https://researchseminars.org/talk/SFUOR/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ben Adcock (Simon Fraser University)
DTSTART:20230126T233000Z
DTEND:20230127T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/8
DESCRIPTION:Title: Re
starts Subject to Approximate Sharpness: a Parameter-free and Optimal Sche
me for Accelerating First-order Methods\nby Ben Adcock (Simon Fraser U
niversity) as part of PIMS-CORDS SFU Operations Research Seminar\n\nLectur
e held in ASB 10908.\n\nAbstract\nSharpness is a generic assumption in con
tinuous optimization that bounds the distance to the set of minimizers in
terms of the suboptimality in the objective function. It leads to the ac
celeration of first-order optimization methods via so-called restarts. How
ever\, sharpness involves problem-specific constants that are typically u
nknown\, and previous restart schemes often result in reduced convergence
rates. Such schemes are also challenging to apply in the presence of nois
e or approximate model classes (e.g.\, in compressed sensing or machine l
earning problems). In this talk\, we introduce the notion of approximate
sharpness\, a generalization of sharpness that incorporates an unknown co
nstant perturbation to the objective function error. By employing a new ty
pe of search over the unknown constants\, we then describe a restart sche
me that applies to general first-order methods. Our scheme maintains the
same convergence rate as when assuming knowledge of the constants. Moreov
er\, for broad classes of problems\, it gives rates of convergence which e
ither match known optimal rates or improve on previously established rate
s. Finally\, we demonstrate the practical efficacy of this scheme on appl
ications including sparse recovery\, compressive imaging and feature sele
ction in machine learning.\n\nThis is joint work with Matthew J. Colbrook
(Cambridge) and Maksym Neyra-Nesterenko (SFU). The corresponding paper ca
n be found here: https://arxiv.org/abs/2301.02268\n
LOCATION:https://researchseminars.org/talk/SFUOR/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hossein Piri (University of Calgary)
DTSTART:20230105T233000Z
DTEND:20230106T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/9
DESCRIPTION:Title: In
dividualized Dynamic Patient Monitoring Under Alarm Fatigue\nby Hossei
n Piri (University of Calgary) as part of PIMS-CORDS SFU Operations Resear
ch Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nHospitals are rife w
ith alarms\, many of which are false. This leads to $alarm$ $fatigue$\,
in which clinicians become desensitized and may inadvertently ignore real
threats. We develop a partially observable Markov decision process model f
or recommending dynamic\, patient-specific alarms in which we incorporate
a $cry$-$wolf$ feedback loop of repeated false alarms. Our model takes in
to account patient heterogeneity in safety limits for vital signs and lear
ns a patient’s safety limits by performing Bayesian updates during a pat
ient’s hospital stay. We develop structural results of the optimal polic
y and perform a numerical case study based on clinical data from an intens
ive care unit. We find that compared with current approaches of setting pa
tients’ alarms\, our dynamic patient-centered model significantly reduce
s the risk of patient harm.\n
LOCATION:https://researchseminars.org/talk/SFUOR/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sandy Rutherford (Simon Fraser University)
DTSTART:20230302T233000Z
DTEND:20230303T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/10
DESCRIPTION:Title: S
imulation Modelling of the BC Critical Care System for Pandemic Response\nby Sandy Rutherford (Simon Fraser University) as part of PIMS-CORDS SF
U Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nT
he pandemic placed considerable stress on the critical care system in Brit
ish Columbia. In this talk\, I will present simulation modelling analysis
done to support the response to the pandemic and ongoing work to improve t
he ability of the critical care system to respond to future public health
crises. The first project that I will discuss is a queuing model to inform
ventilator capacity planning during the first wave of the COVID-19 pandem
ic. I will then describe ongoing development of a discrete event simulatio
n model for the network of major intensive care units (ICUs) in BC. Curren
tly\, our model contains admissions and transfers for ICUs and high acuity
units at eight hospitals in BC. This model will help to improve patient a
ccess to critical care\, and inform planning for seasonal influenza and CO
VID-19.\n
LOCATION:https://researchseminars.org/talk/SFUOR/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Tanmaya Karmarkar (UBC-O hosted) (UBC Okanagan)
DTSTART:20230119T233000Z
DTEND:20230120T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/11
DESCRIPTION:Title: T
ensor Optimization and Applications\nby Tanmaya Karmarkar (UBC-O hoste
d) (UBC Okanagan) as part of PIMS-CORDS SFU Operations Research Seminar\n\
nLecture held in ASB 10908.\n\nAbstract\nFirst example of applying tensor
optimization to combinatorial problems was shown in IPCO 1992: pages 406-4
20. We improve and strengthen those results in several ways and obtain com
putational results on three problems – graph partitioning\, satisfiabili
ty and analysis of counterexamples related to Hilbert’s 17th problem.\n\
nFor this we created a mixed symbolic-numeric model formulation package wh
ich facilitates definition of objective function\, equality and inequality
constraints and definition of new dependent variables.\n\nFor discrete pr
oblems certain inequalities valid at candidate solutions are dynamically i
ncorporated in the iterations of the continuous optimization algorithm bas
ed on underlying non-Newtonian geometry of the interior-point space.\n\nFo
r graph partitioning we obtain optimal solutions including proof of optima
lity. For satisfiability problem we either find the satisfiable assignment
or construct and output proof of unsatisfiability.\n\nFor Hilbert’s 17t
h problem we analyse concrete examples whose non-negativity has been stabl
ished to be not provable using sums of the squares expressions valid in RN
. However\, for these counterexamples\, we construct non-negativity proofs
by computationally constructing sums of squares expressions valid on cert
ain sub-varieties of RN The same modeling package mentioned above is used
to post process the solver output into symbolic proofs of optimality or in
feasibility.\n
LOCATION:https://researchseminars.org/talk/SFUOR/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dimitri Leemans (Université Libre de Bruxelles)
DTSTART:20230202T233000Z
DTEND:20230203T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/12
DESCRIPTION:Title: T
he Number of String C-groups of High Rank\nby Dimitri Leemans (Univers
ité Libre de Bruxelles) as part of PIMS-CORDS SFU Operations Research Sem
inar\n\nLecture held in ASB 10908.\n\nAbstract\nAbstract polytopes are a c
ombinatorial generalisation of classical objects that were already studied
by the greeks. They consist in posets satisfying some extra axioms. Their
rank is roughly speaking the number of layers the poset has. When they ha
ve the highest level of symmetry (namely the automorphism group has one or
bit on the set of maximal chains)\, they are called regular. One can then
use string C-groups to study them.\n\nIndeed\, string C-groups are in one-
to-one correspondence with abstract regular polytopes. They are also smoot
h quotients of Coxeter groups.\n\nThey consist in a pair $(G\,S)$ where $G
$ is a group and $S$ is a set of generating involutions satisfying a strin
g property and an intersection property. The cardinality of the set $S$ is
the rank of the string C-group. It corresponds to the rank of the associa
ted polytope.\n\n \n\nIn this talk\, we will give the latest developments
on the study of string C-groups of high rank. In particular\, if $G$ is a
transitive group of degree $n$ having a string C-group of rank $rgeq (n+3
)/2$\, work over the last twelve years permitted us to show that $G$ is ne
cessarily the symmetric group $S_n$.\n\nWe have just proven in the last mo
nths that if $n$ is large enough\, up to isomorphism and duality\, the num
ber of string C-groups of rank $r$ for $S_n$ (with $r \\geq (n+3)/2$) is t
he same as the number of string C-groups of rank $r+1$ for $S_{n+1}$. \n\
nThis result and the tools used in its proof\, in particular the rank and
degree extension\, imply that if one knows the string C-groups of rank $(n
+3)/2$ for $S_n$ with $n$ odd\, one can construct from them all string C-g
roups of rank $(n+3)/2+k$ for $S_{n+k}$ for any positive integer $k$. \n\
nThe classification of the string C-groups of rank $r \\geq (n+3)/2$ for $
S_n$ is thus reduced to classifying string C-groups of rank $r$ for $S_{2r
-3}$.\n\nA consequence of this result is the complete classification of al
l string C-groups of $S_n$ with rank $n-\\kappa$ for $\\kappa \\in {1\,\\l
dots\,6}$\, when $n \\geq 2 \\kappa+3$\, which extends previous known res
ults.\n\nThe number of string C-groups of rank $n-\\kappa$\, with $n \\geq
2 \\kappa +3$\, of this classification gives the following sequence of in
tegers indexed by $\\kappa$ and starting at $\\kappa = 1$.\n$$\\Sigma{\\ka
ppa}=(1\,1\,7\,9\,35\,48).$$\nThis sequence of integers is new according t
o the On-Line Encyclopedia of Integer Sequences.\n\nJoint work with Peter
J. Cameron (University of St Andrews) and Maria Elisa Fernandes (Universit
y of Aveiro)\n
LOCATION:https://researchseminars.org/talk/SFUOR/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Gohram Baloch (Simon Fraser University)
DTSTART:20230316T223000Z
DTEND:20230316T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/13
DESCRIPTION:Title: D
ata-driven Approach to Optimal Ordering batching Problem in Warehouse Mana
gement\nby Gohram Baloch (Simon Fraser University) as part of PIMS-COR
DS SFU Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstra
ct\nIn this work\, we focus on the picking process in warehouse management
and study it from a data perspective. Using historical data from an indus
trial partner\, we introduce\, model\, and study the robust order batching
problem (ROBP) that groups orders into batches to minimize total order pr
ocessing time accounting for uncertainty caused by system congestion and h
uman behavior. We provide a generalizable\, data-driven approach that over
comes warehouse-specific assumptions characterizing most of the work in th
e literature. We analyze historical data to understand the processes in th
e warehouse\, to predict processing times\, and to improve order processin
g. We introduce the ROBP and develop an efficient learning-based branch-an
d-price algorithm based on simultaneous column and row generation\, embedd
ed with alternative prediction models such as linear regression and random
forest that predict processing time of a batch. We conduct extensive comp
utational experiments to test the performance of the proposed approach and
to derive managerial insights based on real data.\n
LOCATION:https://researchseminars.org/talk/SFUOR/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Angela Morrison (UBCO-hosted) (University of Colorado - Denver)
DTSTART:20230216T233000Z
DTEND:20230217T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/14
DESCRIPTION:Title: O
n Combinatorial Algorithms and Circuit Augmentation for Pseudoflows\nb
y Angela Morrison (UBCO-hosted) (University of Colorado - Denver) as part
of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 10908
.\n\nAbstract\nThere is a wealth of combinatorial algorithms for classical
min-cost flow\nproblems and their simpler variants like max flow or short
est-path problems. It is wellknown\nthat several of these algorithms are i
ntimately related to the Simplex method\nand the more general circuit augm
entation schemes. Prime examples are the network\nSimplex method\, a refin
ement of the primal Simplex method\, and (min-mean) cycle\ncanceling\, whi
ch corresponds to a (steepest-descent) circuit augmentation scheme over\nt
he underlying polyhedron.\n\nWe are interested in deepening and expanding
the understanding of the close relationship\nbetween circuit augmentation
and combinatorial network flows algorithms. To this end\,\nwe generalize f
rom the consideration of primal or dual feasible flows to the so-called\np
seudoflows\, which allow for a violation of flow balance. We introduce wha
t are called\n‘pseudoflow polyhedra’\, in which slack variables are us
ed to quantify this violation\, and\ncharacterize their circuits. This ena
bles us to study various network flows algorithms in\nview of the walks th
at they trace in these polyhedra\, and in view of the pivot rules used\nto
choose the steps.\n\nIn particular\, we show that the Successive Shortest
Path Algorithm and the Shortest/\nGeneric Augmenting Path Algorithm form
general\, non-edge circuit walks. We also\nprovide a proof outline showing
that the aforementioned algorithms correspond to a\nDantzig Rule and Stee
pest-ascent circuit augmentation scheme respectively.\n
LOCATION:https://researchseminars.org/talk/SFUOR/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:[Reading break]
DTSTART:20230223T233000Z
DTEND:20230224T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/15
DESCRIPTION:Title: [
]\nby [Reading break] as part of PIMS-CORDS SFU Operations Research Se
minar\n\nLecture held in ASB 10908.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/SFUOR/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Isabelle Shankar (UBC-O hosted) (Portland State University)
DTSTART:20230525T223000Z
DTEND:20230525T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/16
DESCRIPTION:Title: C
entral Curve in Semidefinite Programming\nby Isabelle Shankar (UBC-O h
osted) (Portland State University) as part of PIMS-CORDS SFU Operations Re
search Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nThe Zariski clos
ure of the central path (which interior point algorithms track in convex o
ptimization\nproblems such as linear and semidefinite programs) is an alge
braic curve\, called the central curve. Its\ndegree has been studied in re
lation to the complexity of these interior point algorithms. We show that\
nthe degree of the central curve for generic semidefinite programs is equa
l to the maximum likelihood\ndegree of linear concentration models. This i
s joint work with Serkan Hosten and Angélica Torres.\n
LOCATION:https://researchseminars.org/talk/SFUOR/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mona Imanpoor Yourdshahy (Simon Fraser University)
DTSTART:20230323T223000Z
DTEND:20230323T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/18
DESCRIPTION:Title: E
ffects of Usage-Based Auto Insurance: A Dynamic Mechanism-Design Approach<
/a>\nby Mona Imanpoor Yourdshahy (Simon Fraser University) as part of PIMS
-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAb
stract\nUsage-Based Insurance (UBI) is one of the most recent innovations
by auto insurance companies that links the premium rates of customers to t
heir actual driving performance. In this program\, drivers’ behaviours a
re monitored directly while they drive. Then\, the insurance company uses
this data to offer discounts on the insurance premium to their customers.
This paper provides a theoretical model to capture the effects of this mon
itoring technology on the auto insurance market. We formulate the underlyi
ng insurance problem as a dynamic principal-agent model with hidden inform
ation and hidden action. An agent (customer) privately knows his type that
summarizes his ability as a driver and can exert an unobservable effort i
n each period\, which affects his subsequent type. The principal (insurer)
offers a long-term contract to the agent despite the fact that she observ
es neither the type of the agent nor the actions he takes. We characterize
the full history-dependent optimal contract for this dynamic adverse sele
ction and moral hazard problem. To compute the optimal contract\, we devel
op a general recursive formulation. The underlying system is a Markov deci
sion process\, where the evolution of the state of the system (type of the
customer) is endogenous\, as it depends on his hidden action in the previ
ous period. We develop a dynamic programming algorithm to examine the mode
l analytically and explore structural results about the optimal contract.
The model results lead to important and interesting managerial insights fo
r firms who may consider offering UBI programs. The study sheds light on h
ow to design the contract to manage a UBI program\, the extent to which a
UBI policy can outperform a traditional policy\, and how the potential gai
ns depend on the demographics of the target market.\n
LOCATION:https://researchseminars.org/talk/SFUOR/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jean-François Cordeau (HEC Montréal)
DTSTART:20230310T233000Z
DTEND:20230311T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/19
DESCRIPTION:Title: T
he Park-and-loop Technician Routing Problem\nby Jean-François Cordeau
(HEC Montréal) as part of PIMS-CORDS SFU Operations Research Seminar\n\n
Lecture held in **SUR 2746**.\n\nAbstract\nMotivated by an application in
the routing of technicians at a\nFrench public utility\, we introduce a hi
ghly efficient heuristic together\nwith a branch-price-and-cut algorithm f
or the doubly open park-and-loop\nrouting problem. This problem is an exte
nsion of the classical vehicle\nrouting problem in which routes may involv
e a main tour performed by driving\na vehicle as well as a set of subtours
that are carried out on foot after\nparking the vehicle. In addition\, ro
utes do not start and end at a central\ndepot\, but rather at customer loc
ations. We first describe a matheuristic\nbased on a split procedure that
generates high quality solutions fast. We\npresent computational experimen
ts on a set of real instances with up to\n3\,800 customers. We also apply
the matheuristic to a related problem called\nthe vehicle routing problem
with transportable resources\, where the method\nfound new best solutions
on 32 out of 40 benchmark instances from the\nliterature. We then present
an exact algorithm\, based on a set-covering\nformulation of the problem w
ith columns representing complete routes\, which\nis capable of solving to
optimality instances with up to 50 customers.\n
LOCATION:https://researchseminars.org/talk/SFUOR/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:[Special talk on Friday in Surrey]
DTSTART:20230309T233000Z
DTEND:20230310T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/20
DESCRIPTION:Title: [
]\nby [Special talk on Friday in Surrey] as part of PIMS-CORDS SFU Ope
rations Research Seminar\n\nLecture held in ASB 10908.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/SFUOR/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marco Caoduro (UBC Vancouver)
DTSTART:20230406T223000Z
DTEND:20230406T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/21
DESCRIPTION:Title: O
n the Packing and Hitting Numbers of Axis-parallel Segments\nby Marco
Caoduro (UBC Vancouver) as part of PIMS-CORDS SFU Operations Research Semi
nar\n\nLecture held in ASB 10908.\n\nAbstract\nGiven a family R of rectang
les in the plane\, the packing number of R\, denoted by $\\nu$(R)\, is the
maximum size of a set of pairwise disjoint rectangles in R\, and the hitt
ing number\, denoted by $\\tau$(R)\, is the minimum size of a set of point
s having a non-empty intersection with each rectangle in R. Clearly\, $\\t
au \\ge \\nu$.\n\nWegner (1965)\, and independently\, Gyárfás and Lehel
(1985)\, asked whether the hitting number $\\tau$ could be bounded by a li
near function of the packing number $\\nu$. In addition\, Wegner proposed
a multiplicative constant of 2. This problem is still wide open and even i
f linear bounds are known for several particular cases\, almost none of th
em are paired with lower bound examples showing their optimality.\n\nFor a
family of axis-parallel line segments\, it is easy to show that $\\tau \\
le 2\\nu$\, as suggested by Wegner. During the talk\, we will consider fam
ilies of axis-parallel segments with the additional property that no three
of them meet at a point (that is\, the intersection graph is triangle-fre
e). We show that\, in this restricted setting\, the packing number of a fa
mily is at least $n/4+C_1\\sqrt{n}$ where $n$ is the size of the considere
d family and $C_1$ is a fixed positive constant. In addition\, we construc
t examples with packing number at most $n/4 + C_2\\sqrt{n}$ for a differen
t constant $C_2 > C_1$ showing that the previous bound is essentially opti
mal.\nAs a consequence of these results\, we settle the Wegner-Gyárfás-L
ehel’s problem for axis-parallel segments showing that the multiplicativ
e constant of 2 is optimal and deduce that $\\tau \\le 2\\nu − C_3 \\sqr
t{\\nu}$ for triangle-free axis-parallel segments. This bound cannot be ac
hieved for triangle-free axis-parallel rectangles\, marking a substantial
difference in the behavior of segments and rectangles.\n\nAt the end of th
e talk\, we will present several open problems\, in particular\, linking o
ur work with the recent developments on the computation of the packing num
ber for axis-parallel rectangles of Mitchell (2021) and Gálvez\, Khan\, M
ari\, Mömke\, Pittu\, and Wiese (2022). This is joint work with Jana Cslo
vjecsek\, Michał Pilipczuk\, and Karol Węgrzycki.\n
LOCATION:https://researchseminars.org/talk/SFUOR/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Zhaosong Lu (Unniversity of Minnesota)
DTSTART:20230921T210000Z
DTEND:20230921T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/22
DESCRIPTION:Title: F
irst-order Methods for Bilevel Optimization\nby Zhaosong Lu (Unniversi
ty of Minnesota) as part of PIMS-CORDS SFU Operations Research Seminar\n\n
Lecture held in ASB 10908.\n\nAbstract\nBilevel optimization has been wide
ly used in a variety of areas such as adversarial training\, hyperparamete
r tuning\, image reconstruction meta-learning\, neural architecture search
\, and reinforcement learning. In this talk\, I will present first-order m
ethods for solving a class of bilevel optimization through the use of sing
le or sequential minimax optimization. The first-order operation complexit
y of the proposed methods will be discussed.\n
LOCATION:https://researchseminars.org/talk/SFUOR/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sharan Vaswani (Simon Fraser University)
DTSTART:20231019T210000Z
DTEND:20231019T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/23
DESCRIPTION:Title: E
xploiting Problem Structure for Efficient Optimization in Machine Learning
\nby Sharan Vaswani (Simon Fraser University) as part of PIMS-CORDS SF
U Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nS
tochastic gradient descent (SGD) is the standard optimization method for t
raining machine learning (ML) models. SGD requires a step-size that depend
s on unknown problem-dependent quantities\, and the choice of this step-si
ze heavily influences the algorithm's practical performance. By exploiting
the interpolation property satisfied by over-parameterized ML models\, we
design a stochastic line-search procedure that can automatically set the
SGD step-size. The resulting algorithm exhibits improved theoretical and e
mpirical convergence\, without requiring the knowledge of any problem-depe
ndent constants. Next\, we consider efficient optimization for imitation l
earning (IL) and reinforcement learning. These settings involve optimizing
functions for which it is expensive to compute the gradient. We propose a
n optimization framework that uses the expensive gradient computation to c
onstruct surrogate functions that can then be minimized efficiently. This
allows for multiple model updates\, thus amortizing the cost of the gradie
nt computation. The resulting majorization-minimization algorithm is equip
ped with strong theoretical guarantees and exhibits fast convergence on st
andard IL problems.\n
LOCATION:https://researchseminars.org/talk/SFUOR/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Julia Yan (UBC Sauder School of Business)
DTSTART:20231102T210000Z
DTEND:20231102T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/24
DESCRIPTION:Title: P
ricing Shared Rides\nby Julia Yan (UBC Sauder School of Business) as p
art of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 1
0908.\n\nAbstract\nShared rides\, which pool individual riders into a sing
le vehicle\, are essential for mitigating congestion and promoting more su
stainable urban transportation. However\, major ridesharing platforms have
long struggled to maintain a healthy and profitable shared rides product.
To understand why shared rides have struggled\, we analyze procedures com
monly used in practice to set static prices for shared rides\, and discuss
their pitfalls. We then propose a pricing policy that is adaptive to matc
hing outcomes\, dubbed match-based pricing\, which varies prices depending
on whether a rider is dispatched alone or to what extent she is matched w
ith another rider. Analysis on a single origin-destination setting reveals
that match-based pricing is both profit-maximizing and altruistic\, simul
taneously improving cost efficiency (i.e.\, the fraction of cost saved by
shared rides relative to individual rides) and reducing rider payments rel
ative to the optimal static pricing policy. These theoretical results are
validated on a large-scale simulation with hundreds of origin-destinations
from Chicago ridesharing data. The improvements in efficiency and reducti
ons in payments are especially noticeable when costs are high and demand d
ensity is low\, enabling healthy operations where they have historically b
een most challenging.\n
LOCATION:https://researchseminars.org/talk/SFUOR/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Heinz Bauschke (UBC-O hosted) (UBC Okanagan)
DTSTART:20231005T210000Z
DTEND:20231005T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/25
DESCRIPTION:Title: O
n the Composition of Two Linear Projections\nby Heinz Bauschke (UBC-O
hosted) (UBC Okanagan) as part of PIMS-CORDS SFU Operations Research Semin
ar\n\nLecture held in ASB 10908.\n\nAbstract\nProjection operators are fun
damental algorithmic operators in Analysis and Optimization. It is well kn
own that these operators are firmly nonexpansive\; however\, their compos
ition is generally only averaged and no longer firmly nonexpansive. We wi
ll introduce the modulus of averagedness and provide an exact result for t
he composition of two linear projection operators. As a consequence\, we d
educe that the Ogura-Yamada bound for the modulus of the composition is sh
arp. Based on joint work with Theo Bendit and Walaa Moursi.\n
LOCATION:https://researchseminars.org/talk/SFUOR/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Feyza Sahinyazan (SFU Beedie School of Business)
DTSTART:20231116T220000Z
DTEND:20231116T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/26
DESCRIPTION:Title: P
ath to Energy Sovereignty: Clean and Affordable Solutions for Remote Commu
nities\nby Feyza Sahinyazan (SFU Beedie School of Business) as part of
PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 10908.\
n\nAbstract\nRemote communities around the globe rely on off-grid installa
tions of stand-alone diesel generators to cover their energy needs\, which
can be costly\, harmful to the environment and subject to disruptions. Po
licymakers seek sustainable solutions for these communities to meet the Su
stainable Development Goals regarding clean energy and reduced inequalitie
s. Even with the best intentions\, ignoring community perspectives can ham
per the clean energy transition and energy accessibility goals of remote c
ommunities. Our objective in this research is to identify the optimal gene
ration capacity investment decisions from a remote community’s perspecti
ve and investigate how common policy mechanisms interact with these decisi
ons.\n\n\nI am also planning to dedicate some portion of my talk to give a
brief overview of my other ongoing projects to see if there is any intere
st in collaboration.\n
LOCATION:https://researchseminars.org/talk/SFUOR/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Frederik Kunstner (UBC)
DTSTART:20231130T220000Z
DTEND:20231130T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/27
DESCRIPTION:Title: S
earching for Optimal Per-Coordinate Step-sizes with Multidimensional Backt
racking\nby Frederik Kunstner (UBC) as part of PIMS-CORDS SFU Operatio
ns Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nThe backtra
cking line-search is an effective technique to automatically tune the step
-size in smooth optimization. It guarantees similar performance to using t
he theoretically optimal step-size. Many approaches have been developed to
instead tune per-coordinate step-sizes\, also known as diagonal precondit
ioners\, but none of the existing methods are provably competitive with th
e optimal per-coordinate stepsizes. We propose multidimensional backtracki
ng\, an extension of the backtracking line-search to find good diagonal pr
econditioners for smooth convex problems. Our key insight is that the grad
ient with respect to the step-sizes\, also known as hypergradients\, yield
s separating hyperplanes that let us search for good preconditioners using
cutting-plane methods. As black-box cutting-plane approaches like the ell
ipsoid method are computationally prohibitive\, we develop an efficient al
gorithm tailored to our setting. Multidimensional backtracking is provably
competitive with the best diagonal preconditioner and requires no manual
tuning.\n
LOCATION:https://researchseminars.org/talk/SFUOR/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Eitan Levin (UBC-O hosted) (California Institute of Technology)
DTSTART:20240125T220000Z
DTEND:20240125T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/28
DESCRIPTION:Title: A
ny-dimensional Convex Sets\nby Eitan Levin (UBC-O hosted) (California
Institute of Technology) as part of PIMS-CORDS SFU Operations Research Sem
inar\n\nLecture held in ASB 10908.\n\nAbstract\nClassical algorithms are d
efined on inputs of different sizes. In contrast\, data-driven algorithms\
, that is\, algorithms learned from some data\, may only be defined on inp
uts of the same size as the data. What\ndoes it mean for an algorithm to b
e defined on infinitely-many input sizes? How do we describe such\nalgorit
hms\, and how do we parametrize and search over them?\n\nIn this talk\, we
tackle these questions for convex optimization-based algorithms. Describi
ng such\nalgorithms reduces to describing convex sets. These\, in turn\, a
re often "freely" described\, meaning that their description makes instant
iation in every dimension obvious. Examples include unit balls of\nstandar
d norms defined on vectors of any size\, graph parameters defined for grap
hs of any size\, and\n(quantum) information theoretic quantities defined f
or distributions on any number of (qu)bits.\n\nWe show that such free desc
riptions of convex sets arise from two ingredients. First\, group invarian
ce\nand the recently-identified phenomenon of representation stability. Se
cond\, embeddings and projections\nrelating different-sized problem instan
ces. We combine these ingredients to obtain parametrized\nfamilies of infi
nitely instantiable convex sets. To extend a set learned from data in a fi
xed dimension to higher ones\, we identify consistency conditions relating
sets in different dimensions that are satisfied in a variety of applicati
ons\, and obtain parametrizations respecting these conditions. Our paramet
rizations can be obtained computationally.\n
LOCATION:https://researchseminars.org/talk/SFUOR/28/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Warren Hare (UBC-O hosted) (UBC Okanagan)
DTSTART:20240111T220000Z
DTEND:20240111T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/29
DESCRIPTION:Title: E
xpected Decrease for Derivative-free Algorithms Using Random Subspaces
\nby Warren Hare (UBC-O hosted) (UBC Okanagan) as part of PIMS-CORDS SFU O
perations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nDeri
vative-free algorithms seek the minimum of a given function based only on
function values queried at appropriate points. Their performance is known
to worsen as the problem dimension increases.\n \nRecent advances in dev
eloping randomized derivative-free techniques have tackled this issue by w
orking in low-dimensional subspaces that are drawn at random in an iterati
ve fashion. In this talk\, we present analysis for derivative-free algori
thms that employing random subspaces to obtain understanding of the expect
ed decrease achieved per function evaluation.\n
LOCATION:https://researchseminars.org/talk/SFUOR/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Charles Audet (UBC-O hosted) (Polytechnique Montréal)
DTSTART:20240314T210000Z
DTEND:20240314T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/30
DESCRIPTION:Title: E
volution of the Mads Algorithm by Developing Specific Features\nby Cha
rles Audet (UBC-O hosted) (Polytechnique Montréal) as part of PIMS-CORDS
SFU Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\
nThe topic of this talk is on Blackbox optimization (BBO)\, the study of\n
applications\, attributes\, and solutions of optimization problems in\nwhi
ch the values of one or more of the functions defining the problem\nare pr
ovided through blackboxes. The Mesh Adaptive Direct Search (Mads)\nis a de
rivative-free algorithm pioneered in 2006 for constrained BBO\nproblems. T
his talk discusses recent advances to the Mads algorithm\,\nincluding -i-
the treatment of granular variables\; -ii- dynamic scaling\nof variables\;
-iii- escaping unknown discontinuities\; and -iv- revised\nconvergence re
sults for discontinuous functions.\n
LOCATION:https://researchseminars.org/talk/SFUOR/30/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yves Lucet (UBC-O hosted) (UBC-Okanagan)
DTSTART:20240229T220000Z
DTEND:20240229T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/31
DESCRIPTION:Title: R
ecent Results in Computational Convex Analysis\nby Yves Lucet (UBC-O h
osted) (UBC-Okanagan) as part of PIMS-CORDS SFU Operations Research Semina
r\n\nLecture held in ASB 10908.\n\nAbstract\nComputational convex analysis
aims at computing mathematical objects commonly used in convex analysis w
ith an emphasis on lower dimensions to facilitate visualization. The lates
t contributions have focused on computing the closest convex function and
the ongoing quest for explicit formulas for the conjugate of nonconvex fun
ctions. To ease the development of a toolbox\, greater emphasis is put on
simplifying intermediate computations especially for piecewise functions b
ecause the overall computation time depends on the number of pieces.\n
LOCATION:https://researchseminars.org/talk/SFUOR/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yiwen Chen (UBC-O hosted) (UBC Okanagan)
DTSTART:20240404T210000Z
DTEND:20240404T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/32
DESCRIPTION:Title: Q
-fully quadratic modeling and its application in a random subspace derivat
ive-free method\nby Yiwen Chen (UBC-O hosted) (UBC Okanagan) as part o
f PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 10908.
\n\nAbstract\nDerivative-free optimization (DFO) methods are a class of op
timization methods that do not use the derivatives of the objective or con
straint functions. Model-based DFO methods are an important class of DFO
methods that are known to struggle with solving high-dimensional optimizat
ion problems. Recent research has shown that incorporating random subspac
es into model-based DFO methods has the potential to improve their perform
ance on high-dimensional problems. However\, most of the current theoretic
al and practical results are based on linear approximation models due to t
he complexity of quadratic approximation models. In this talk\, we propose
a random subspace derivative-free trust-region algorithm based on quadrat
ic approximations. Unlike most of its precursors\, this algorithm does not
require any special form of objective function. We study the geometry of
sample sets\, the error bounds for approximations\, and the quality of sub
spaces. In particular\, we provide a technique to construct Q-fully quadra
tic models\, which is easy to analyze and implement. We present an almost-
sure global convergence result of our algorithm and give an upper bound on
the expected number of iterations to find a sufficiently small gradient.
We also develop numerical experiments to compare the performance of our al
gorithm using both linear and quadratic approximation models. The numerica
l results demonstrate the strengths and weaknesses of using quadratic appr
oximations.\n
LOCATION:https://researchseminars.org/talk/SFUOR/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Fatemeh Beik (Vali-e-Asr University of Rafsanjan)
DTSTART:20240509T210000Z
DTEND:20240509T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/33
DESCRIPTION:Title: A
survey on preconditioning techniques for a class of block three-by-three
linear systems\nby Fatemeh Beik (Vali-e-Asr University of Rafsanjan) a
s part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in AS
B 10908.\n\nAbstract\nIn this talk\, we study the performance of some prec
onditioners for accelerating the convergence of Krylov subspace methods fo
r solving linear systems of equations with a block three-by-three structur
e. A brief discussion is included regarding how spectral and field-of-valu
e analyses can be exploited to study the performance of a preconditioner i
n conjunction with the Generalized Minimum Residual Method (GMRES). Numeri
cal experiments show the effectiveness of inexact versions of precondition
ers used with flexible GMRES for solving linear systems of equations arisi
ng from mixed finite element discretizations of the coupled Stokes-Darcy f
low problem.\n
LOCATION:https://researchseminars.org/talk/SFUOR/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:João Gouveia (UBC-O hosted) (University of Coimbra)
DTSTART:20240425T210000Z
DTEND:20240425T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/34
DESCRIPTION:Title: S
elf-dual polyhedral cones and their slack matrices\nby João Gouveia (
UBC-O hosted) (University of Coimbra) as part of PIMS-CORDS SFU Operations
Research Seminar\n\nLecture held in ASB 10908.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/SFUOR/34/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Benny Wai (Pattison Food Group Ltd.)
DTSTART:20240418T210000Z
DTEND:20240418T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/35
DESCRIPTION:Title: D
ata Science Initiatives in the Grocery Industry\nby Benny Wai (Pattiso
n Food Group Ltd.) as part of PIMS-CORDS SFU Operations Research Seminar\n
\nLecture held in ASB 10908.\n\nAbstract\nPattison Food Group Ltd.\, encom
passing Save-On-Food\, Nester’s Market\, and over a dozen other grocery
brands\, stands as a leading provider of food and health products in Weste
rn Canada. In this presentation\, Benny Wai\, Data & Analytics Manager at
PFG and SFU alumnus\, will delve into the machine learning and optimizatio
n solutions currently implemented at PFG. We will also navigate the comple
xities and growing pains associated with establishing a new D&A department
within an organization with over a century of history\, highlighting the
transformative journey towards data-driven decision-making.\n
LOCATION:https://researchseminars.org/talk/SFUOR/35/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yohan Song (University of Waterloo)
DTSTART:20240620T213000Z
DTEND:20240620T223000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/36
DESCRIPTION:Title: A
Combinatorial Puzzle of Skew Shapes\nby Yohan Song (University of Wat
erloo) as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture he
ld in ASB 10908.\n\nAbstract\nA skew shape is a difference of two Young di
agrams where one diagram contains the other. In 2017\, Jenna Rajchgot\, Ma
tthew Satriano\, and Wanchun Shen showed that skew shapes can be used to s
tudy the Gerstenhaber problem\, a matrix algebra problem in commutative al
gebra. In this talk\, I present a recent progress with this method\, albei
t the approach is purely combinatorial.\n
LOCATION:https://researchseminars.org/talk/SFUOR/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Curtis Bright (University of Windsor)
DTSTART:20240822T210000Z
DTEND:20240822T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/37
DESCRIPTION:Title: S
AT and Lattice Reduction for Integer Factorization\nby Curtis Bright (
University of Windsor) as part of PIMS-CORDS SFU Operations Research Semin
ar\n\nLecture held in ASB 10908.\n\nAbstract\nThe difficulty of factoring
large integers into primes is the basis for cryptosystems such as RSA. Due
to the widespread popularity of RSA\, there have been many proposed attac
ks on the factorization problem such as side-channel attacks where some bi
ts of the prime factors are available. When enough bits of the prime facto
rs are known\, two methods that are effective at solving the factorization
problem are satisfiability (SAT) solvers and Coppersmith's method. The SA
T approach reduces the factorization problem to a Boolean satisfiability p
roblem\, while Coppersmith's approach uses lattice basis reduction. Both m
ethods have their advantages\, but they also have their limitations: Coppe
rsmith's method does not apply when the known bit positions are randomized
\, while SAT-based methods can take advantage of known bits in arbitrary l
ocations\, but have no knowledge of the algebraic structure exploited by C
oppersmith's method. In this paper we describe a new hybrid SAT and comput
er algebra approach to efficiently solve random leaked-bit factorization p
roblems. Specifically\, Coppersmith's method is invoked by a SAT solver to
determine whether a partial bit assignment can be extended to a complete
assignment. Our hybrid implementation solves random leaked-bit factorizati
on problems significantly faster than either a pure SAT or pure computer a
lgebra approach.\n\nThis is joint work with Yameen Ajani.\n
LOCATION:https://researchseminars.org/talk/SFUOR/37/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ahmadreza Marandi (UBC-O hosted) (Eindhoven University)
DTSTART:20240828T210000Z
DTEND:20240828T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/38
DESCRIPTION:Title: A
Clustering-based uncertainty set for Robust Optimization\nby Ahmadrez
a Marandi (UBC-O hosted) (Eindhoven University) as part of PIMS-CORDS SFU
Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nRob
ust Optimization (RO) is an approach to tackle uncertainties in the parame
ters of an optimization\nproblem. Constructing an uncertainty set is cruci
al for RO\, as it determines the quality and the\nconservativeness of the
solutions. In this talk\, we introduce an approach for constructing a data
-driven\nuncertainty set through volume-based clustering\, which we call M
inimum-Volume Norm-Based\nClustering (MVNBC)\, that leads to less conserva
tive solutions. MVNBC extends the concept of\nMinimum-Volume Ellipsoid Clu
stering by enabling customizable regions containing clusters. These\nregio
ns are defined based on a given set of vector norms\, hence providing grea
t flexibility in capturing\ndiverse data patterns. We formulate a mixed-in
teger conic optimization problem for MVNBC. To\naddress computational comp
lexities\, we design an efficient iterative approximation algorithm where
we\nreassign points to clusters and improve the volume of the regions. Our
numerical experiments\ndemonstrate the effectiveness of our approach in c
apturing data patterns and finding clusters with\nminimum total volume. Mo
reover\, constructed uncertainty sets based on MVNBC result in robust\nsol
utions with 10% improvement in the objective value compared to the ones ob
tained by a recent datadriven\nuncertainty set. Therefore\, using our unce
rtainty sets in RO problems can generate less\nconservative solutions comp
ared to traditional uncertainty sets as well as other existing data-driven
\napproaches.\n
LOCATION:https://researchseminars.org/talk/SFUOR/38/
END:VEVENT
BEGIN:VEVENT
SUMMARY:16 speakers (Held at UBC Vancouver)
DTSTART:20240921T153000Z
DTEND:20240921T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/39
DESCRIPTION:Title: 2
024 West Coast Optimization Meeting\nby 16 speakers (Held at UBC Vanco
uver) as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture hel
d in 110 Hugh Dempster Pavilion.\n\nAbstract\nDetails at: \n
LOCATION:https://researchseminars.org/talk/SFUOR/39/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Gabriel Jarry-Bolduc (UBC-O hosted) (Mount Royal University)
DTSTART:20241017T210000Z
DTEND:20241017T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/40
DESCRIPTION:Title: T
he cosine measure relative to a subspace\nby Gabriel Jarry-Bolduc (UBC
-O hosted) (Mount Royal University) as part of PIMS-CORDS SFU Operations R
esearch Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nThe cosine meas
ure is a tool that tells you how well a set of directions is covering the
space $\\mathbb{R}^n$. In this talk\, we extend the concept of cosine meas
ure by defining the cosine measure relative to a subspace.\nThis novel def
inition might be useful for subspace decomposition optimization methods. W
e propose a\ndeterministic algorithm to compute it and discuss the situati
on in which the set of directions is infinite.\n
LOCATION:https://researchseminars.org/talk/SFUOR/40/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Shawn Wang (UBC-O hosted) (UBC-Okanagan)
DTSTART:20241003T210000Z
DTEND:20241003T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/41
DESCRIPTION:Title: O
n Bauschke-Bendit-Moursi modulus of averagedness\nby Shawn Wang (UBC-O
hosted) (UBC-Okanagan) as part of PIMS-CORDS SFU Operations Research Semi
nar\n\nLecture held in ASB 10908.\n\nAbstract\nFirmly nonexpansive operato
rs are important in Convex Analysis and Optimization and Algorithms. It is
a special case of averaged operators. We classify averaged operators\, fi
rmly nonexpansive operators\, and proximal mappings by\nthe BBW modulus of
averagedness. One amazing result is that a proximal mapping of a convex f
unction has its modulus of averagedness less than $1/2$ if and only if the
function is Lipschitz smooth. Joint work with Shuang Song.\n
LOCATION:https://researchseminars.org/talk/SFUOR/41/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nitya Mani (UBC-O hosted) (MIT)
DTSTART:20241107T220000Z
DTEND:20241107T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/42
DESCRIPTION:Title: T
etrahedron intersecting families of hypergraphs\nby Nitya Mani (UBC-O
hosted) (MIT) as part of PIMS-CORDS SFU Operations Research Seminar\n\nLec
ture held in ASB 10908.\n\nAbstract\nAn $H$-intersecting family of 3-unifo
rm hypergraphs on $n$ labelled vertices is a family of hypergraphs $\\math
cal{F}$ such that for any pair of hypergraphs $G_1\, G_2 \\in \\mathcal{F}
$\, the intersection $G_1 \\cap G_2$ contains a copy of $H$ as a subgraph.
One can construct a large such family $\\mathcal{F}$ by choosing all of t
he hypergraphs that contain a fixed copy of $H$\, a family with size $2^{{
n \\choose 3} - e(H)}$. Understanding for which cases such a family is asy
mptotically maximal is a very old and well-studied question\, and it has b
een conjectured that this lower bound is tight whenever $H$ is a complete
graph. The case of triangle intersecting families of graphs was studied by
Shearer and was one of the first application's of Shearer’s entropy ine
quality to a combinatorial problem. This triangle-intersecting problem was
fully resolved by Ellis\, Filmus\, and Friedgut\, and more recently the c
ase of $K_4$-intersecting graphs was resolved by Berger and Zhao\, both us
ing linear programming bounds. Despite this progress\, understanding the m
aximal size of an $H$-intersecting family remains open for every other com
plete (hyper)graph. In join work with Owen Zhang\, we resolve the case of
$K_5$-intersecting families and provide the first resolution of an instanc
e in the hypergraph setting\, showing that the conjecture holds for tetrah
edron-intersecting families of 3-uniform hypergraphs.\n
LOCATION:https://researchseminars.org/talk/SFUOR/42/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Betty Shea (UBC Vancouver)
DTSTART:20241114T220000Z
DTEND:20241114T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/43
DESCRIPTION:Title: W
hy Line-Search When You Can Plane-Search?\nby Betty Shea (UBC Vancouve
r) as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held i
n ASB 10908.\n\nAbstract\nThe practical performance of an optimization met
hod depends on details such as using good step sizes. Strategies for setti
ng step sizes are generally limited to hyperparameter tuning (for a fixed
step size)\, step size schedules and line searches. For many common machin
e learning problems\, line optimization and subspace optimization find acc
urate step sizes for asymptotically the same cost as using a fixed step si
ze. In some cases\, line optimization may find step sizes that are ruled o
ut by the standard Armijo condition. For optimization methods that use mul
tiple search directions\, such as gradient descent with momentum\, using s
ubspace optimization instead of fixed step size schedules allow for better
adaptivity and potentially faster convergence. In the case of some neural
networks\, subspace optimization allows the use of different step sizes f
or different layers that could decrease the amount of training time needed
\, as well as reducing the dependence on hyperparameter tuning.\n
LOCATION:https://researchseminars.org/talk/SFUOR/43/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sandy Rutherford (SFU)
DTSTART:20241128T220000Z
DTEND:20241128T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/44
DESCRIPTION:Title: C
ritical Care Planning for Pandemic Response\nby Sandy Rutherford (SFU)
as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in
ASB 10908.\n\nAbstract\nThe COVID-19 Pandemic placed considerable strain o
n intensive care units\, and the critical care system in British Columbia
and worldwide. I will review the critical care system and how it responded
to the COVID-19 pandemic. During the first wave of the COVID-19 Pandemic\
, we developed a simulation model to inform mechanical ventilator access i
n BC. One of the challenges that we faced is that simulation models are di
fficult to study under epidemic growth in demand. I will describe approxim
ation methods from queueing theory that we used to address this challenge.
Specifically\, I will explore the accuracy of three queueing theory appro
ximations under epidemic growth in demand\, namely: the pointwise stationa
ry approximation\, the modified offered load approximation\, and the fixed
-point approximation. We found that the fixed-point approximation is the m
ost accurate and a hybrid optimization approach combining the fixed-point
approximation with simulation optimization was developed to determine the
number of mechanical ventilators required to ensure that at least 95% of p
atients could access a ventilator immediately during the first wave of the
COVOD-19 pandemic. Strengthening the BC critical care system to respond t
o seasonal respiratory illnesses and future pandemics is a priority of the
Ministry of Health. I will describe a large-scale simulation model that w
e have developed to support this effort and discuss how operations researc
h can contribute to improving quality of care\, efficiency\, and resilienc
y in the critical care system.\n
LOCATION:https://researchseminars.org/talk/SFUOR/44/
END:VEVENT
BEGIN:VEVENT
SUMMARY:[Halloween]
DTSTART:20241031T210000Z
DTEND:20241031T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/45
DESCRIPTION:by [Halloween] as part of PIMS-CORDS SFU Operations Research S
eminar\n\nLecture held in ASB 10908.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/SFUOR/45/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jiajin Li (UBC Sauder)
DTSTART:20250123T220000Z
DTEND:20250123T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/46
DESCRIPTION:Title: U
nveiling Spurious Stationarity and Hardness Results for Bregman Proximal-T
ype Algorithms\nby Jiajin Li (UBC Sauder) as part of PIMS-CORDS SFU Op
erations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nBregm
an proximal-type algorithms\, such as mirror descent\, are popular in opti
mization and data science for effectively exploiting problem structures an
d optimizing them under tailored geometries. However\, most of existing co
nvergence results rely on the gradient Lipschitz continuity of the kernel\
, which unfortunately excludes most commonly used cases\, such as the Shan
non entropy. In this paper\, we reveal a fundamental limitation of these m
ethods: Spurious stationary points inevitably arise when the kernel is n
ot gradient Lipschitz. The existence of these spurious stationary points l
eads to an algorithm-dependent hardness result: Bregman proximal-type algo
rithms cannot escape from a spurious stationary point within any finite nu
mber of iterations when initialized from that point\, even in convex setti
ngs. This limitation is discovered through the lack of a well-defined stat
ionarity measure based on Bregman divergence for non-gradient Lipschitz ke
rnels. Although some extensions attempt to address this issue\, we demonst
rate that they still fail to reliably distinguish between stationary and n
on-stationary points for such kernels. Our findings underscore the need fo
r new theoretical tools and algorithms in Bregman geometry\, paving the wa
y for further research.\n
LOCATION:https://researchseminars.org/talk/SFUOR/46/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Serhii Myroshnychenko (University of the Fraser Valley)
DTSTART:20241024T210000Z
DTEND:20241024T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/47
DESCRIPTION:Title: C
entroid of a convex body can be rarely the centroid of its sections\n
by Serhii Myroshnychenko (University of the Fraser Valley) as part of PIMS
-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAb
stract\nWe construct a convex body $K$ in $R^n$\, $n \\ge 5$\, with the pr
operty that there is exactly one hyperplane $H$ passing through $c(K)$\, t
he centroid of $K$\, such that the centroid of $K \\cap H$ coincides with
$c(K)$. This provides answers to questions of Grunbaum and Loewner for $n
\\ge 5$. The proof is based on the existence of non-intersection bodies in
these dimensions. Joint work with K. Tatarko and V. Yaskin.\n
LOCATION:https://researchseminars.org/talk/SFUOR/47/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kevin Hare (UBC-O hosted) (University of Waterloo)
DTSTART:20241205T220000Z
DTEND:20241205T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/48
DESCRIPTION:Title: C
omputational Progress on the Unfair 0-1 Polynomial Conjecture\nby Kevi
n Hare (UBC-O hosted) (University of Waterloo) as part of PIMS-CORDS SFU O
perations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nLet
$c(x)$ be a monic integer polynomial with coefficients 0 or 1. Write\n$c(x
)=a(x)b(x)$ where $a(x)$ and $b(x)$ are monic polynomials with\nnon-negati
ve real (not necessarily integer) coefficients. The unfair\n0-1 polynomial
conjecture states that $a(x)$ and $b(x)$ are necessarily\ninteger polynom
ials with coefficients 0 or 1.\n
LOCATION:https://researchseminars.org/talk/SFUOR/48/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Krishna Narayanan (SFU)
DTSTART:20241212T220000Z
DTEND:20241212T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/49
DESCRIPTION:Title: C
oping with Intractability: Parameterized Algorithms meets Linear Programmi
ng\nby Krishna Narayanan (SFU) as part of PIMS-CORDS SFU Operations Re
search Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nThis seminar wil
l highlight the role of linear programming techniques in the design of par
ametrized algorithms as a framework to cope with intractability\, which I
will attempt to motivate. After a brief introduction\, I will also briefly
talk about the associated notion of kernelization\, which transforms inpu
t instances into more “manageable forms”. I will then demonstrate how
linear programming is applied in lieu of this framework to obtain a reason
able algorithm from a parametrized complexity perspective for an otherwise
intractable problem like vertex cover. Lastly\, recent advancements that
use similar techniques for vertex cover will also be mentioned. This prese
ntation is part of my graduate coursework on Discrete Optimization.\n
LOCATION:https://researchseminars.org/talk/SFUOR/49/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mariana Resener (SFU SEE)
DTSTART:20250206T220000Z
DTEND:20250206T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/50
DESCRIPTION:Title: O
ptimization in Active Distribution Grids: Modelling\, Uncertainty\, and Ap
plications\nby Mariana Resener (SFU SEE) as part of PIMS-CORDS SFU Ope
rations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nThe ch
allenge of solving energy distribution grid planning problems\, including
operation and expansion planning\, stems from their combinatorial nature a
nd the vast solution space. Several models and techniques have been propos
ed in the literature to tackle these challenges. This talk explores optimi
zation techniques and their applications in active distribution systems\,
with a focus on modelling devices and distributed energy resources (DERs)\
, along with the assumptions and simplifications involved. It highlights t
he integration of these models into key optimization problems. Key topics
include a taxonomy of optimal power flow and the linearization of grid dev
ice models for use in mixed-integer linear programming. The presentation a
lso provides an overview of methods for accounting for uncertainties intro
duced by DERs and load variations in optimization models.\n
LOCATION:https://researchseminars.org/talk/SFUOR/50/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jessica Stockdale (SFU)
DTSTART:20250306T220000Z
DTEND:20250306T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/51
DESCRIPTION:Title: U
sing Topological Features of Phylogenetic Trees to Inform Seasonal Influen
za Vaccine Design\nby Jessica Stockdale (SFU) as part of PIMS-CORDS SF
U Operations Research Seminar\n\nLecture held in ASB 10908.\nAbstract: TBA
\n
LOCATION:https://researchseminars.org/talk/SFUOR/51/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jas Dhahan (SFU)
DTSTART:20250320T210000Z
DTEND:20250320T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/52
DESCRIPTION:Title: S
imulation modelling to inform group O negative red blood cell inventory ma
nagement in British Columbia\nby Jas Dhahan (SFU) as part of PIMS-CORD
S SFU Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstrac
t\nBlood is a crucial life-saving product in healthcare systems. Red blood
cells are perishable\, and managing these stocks in British Columbia and
other regions of Canada\, with remote / rural hospitals is challenging. De
mand must be satisfied without wasting this resource. Group O negative red
blood cells are a precious resource because they can be donated universal
ly. O negative individuals comprise 6-7% of our general population\, yet O
negative demand exceeds 12% of transfusions. There is growing concern ove
r the sustainability of the O negative supply. The appropriate management
of even a single red blood cell unit has the potential to save a life. \n\
nThere are seven health authorities in British Columbia with over 80 hospi
tals that manage their own blood inventory. British Columbia is a complex
jurisdiction\, which operates a provincial redistribution program\, where
red blood cells near expiry are sent from smaller to larger sites for use
before expiring to minimize wastage.\n\nIn this talk\, we discuss our ongo
ing collaboration with the Provincial Blood Coordination Office in British
Columbia and Canadian Blood Services to inform red blood cell inventory m
anagement. We capture the key characteristics of a redistribution network
of hospital blood banks using a stochastic queue network model. Our model
is calibrated to and validated against real-world data from the Transparen
t Blood Inventory Database. This work is funded by NSERC and the Canadian
Blood Services Blood Efficiency Accelerator Program.\n
LOCATION:https://researchseminars.org/talk/SFUOR/52/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Walaa Moursi (UBC-O hosted) (University of Waterloo)
DTSTART:20250313T210000Z
DTEND:20250313T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/53
DESCRIPTION:Title: C
hambolle-Pock algorithm revisited: splitting operator and its range with a
pplications\nby Walaa Moursi (UBC-O hosted) (University of Waterloo) a
s part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in AS
B 10908.\n\nAbstract\nPrimal-dual hybrid gradient (PDHG) is a first-order
method for saddle-point problems and convex\nprogramming introduced by Cha
mbolle and Pock. Recently\, Applegate et al. analyzed the behavior of\nPDH
G when applied to an infeasible or unbounded instance of linear programmin
g\, and in particular\,\nshowed that PDHG is able to diagnose these condit
ions. Their analysis hinges on the notion of the\ninfimal displacement vec
tor in the closure of the range of the displacement mapping of the splitti
ng\noperator that encodes the PDHG algorithm. In this talk\, we develop a
novel formula for this range using\nmonotone operator theory. The analysis
is then specialized to conic programming and further to\nquadratic progra
mming (QP) and second-order cone programming (SOCP). A consequence of our\
nanalysis is that PDHG is able to diagnose infeasible or unbounded instanc
es of QP and of the ellipsoid-separation problem\, a subclass of SOCP.\n
LOCATION:https://researchseminars.org/talk/SFUOR/53/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sean Kafer (remote) (Georgia Tech)
DTSTART:20250424T210000Z
DTEND:20250424T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/54
DESCRIPTION:Title: S
olving 0/1 Linear Programs in Strongly Polynomial Time with Simplex\nb
y Sean Kafer (remote) (Georgia Tech) as part of PIMS-CORDS SFU Operations
Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nThe Simplex me
thod for solving linear programs (LPs) is one of the most widely used LP s
olvers due to\nthe fact that it runs very quickly in practice. However\, d
espite its practical efficiency and decades of study\, it remains unknown
whether or not it provably runs in polynomial time. Other methods for\nsol
ving LPs are known to run in so-called weakly polynomial time for general
LPs\, but few such results\nare known for the Simplex method even for very
restrictive and well-known subclasses of LPs.\n\nIn this talk\, I will di
scuss the special subclass of 0/1 LPs\, i.e.\, those whose vertex solution
s have\ncomponents in {0\,1}. These are an important and widely studied cl
ass of LPs which model many\nproblems from combinatorial optimization. Eve
n for this important class\, the performance of the\nSimplex method on the
se LPs remained unknown for decades. I will discuss the history of their s
tudy as\nit pertains to the Simplex method\, and I will present a series o
f results by Alex Black\, Jesús De Loera\,\nLaura Sanità\, and myself wh
ich culminate in a proof that the Simplex method can solve 0/1 LPs in\nstr
ongly polynomial time.\n
LOCATION:https://researchseminars.org/talk/SFUOR/54/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anotida Madzvamuse (UBC-O hosted) (UBC)
DTSTART:20250529T210000Z
DTEND:20250529T220000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/55
DESCRIPTION:Title: U
sing geometric bulk-surface PDEs for insilico modelling of single and coll
ective cell migration\nby Anotida Madzvamuse (UBC-O hosted) (UBC) as p
art of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 1
0908.\n\nAbstract\nIn this talk\, I will present insilico models for 2- an
d 3-D cell migration\, from single to collective\, based on geometric bulk
-surface partial differential equations (G-BS-PDEs). The first model is a
geometric surface PDE approach where the cell is described by its cell mem
brane which obeys a force balance equation for its evolution. This approac
h encodes naturally the biochemical processes and biomechanical properties
of the cells and its interactions with deformable obstacles\, and cell-to
-cell interactions. I will also present a generalisation to include interi
or cell dynamics for cells migrating in confinement. The second model cons
ists of an optimal control model based on geometric multigrid methods for
a diffuse-interface formulation. This approach allows us to model the spat
iotemporal dynamics of static experimental images of migrating cells. A by
-product of this methodology is the automatic quantification of proliferat
ion rates associated with cell division. A third and final approach is a v
iscoelastic model\, where the displacements of the cell are driven by biom
olecular species which obey a reaction-diffusion system. Numerical results
will be presented to illustrate the novelty of these mechanobiochemical m
odels for single and collective cell migration. Single and collective cell
migration are essential for physiological\, pathological and biomedical p
rocesses in development\, repair\, and disease\, for example\, in embryoge
nesis\, wound healing\, immune response\, cancer metastasis\,\ntumour inva
sion\, and inflammation.\n
LOCATION:https://researchseminars.org/talk/SFUOR/55/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Clement Royer (UBC-O hosted\, on-line only) (Université Paris Dau
phine-PSL)
DTSTART:20250729T180000Z
DTEND:20250729T190000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/56
DESCRIPTION:Title: A
derivative-free method for continuous submodular optimization\nby Cle
ment Royer (UBC-O hosted\, on-line only) (Université Paris Dauphine-PSL)
as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in A
SB 10908.\n\nAbstract\nSubmodular functions are a classical concept of dis
crete optimization\, that can also be extended to the\ncontinuous setting.
In particular\, the class of continuous submodular functions encompasses
some\nnonconvex functions arising in natural language processing\, which p
artly explains renewed interest for\nthis topic in recent years.\n\nIn thi
s talk\, I will describe a derivative-free algorithm for continuous submod
ular optimization\nover compact sets\, adapted from a classical framework
for bound-constrained derivative-free\noptimization. The first part will f
ocus on theoretical (complexity) guarantees for the proposed method\,\nwhi
ch departs from the general nonconvex setting. The second part will illust
rate the practical\nperformance of our algorithm on continuous submodular
tasks. Time permitting\, I will also discuss the\ndiscrete submodular case
.\n
LOCATION:https://researchseminars.org/talk/SFUOR/56/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ali Hassanzadeh (jointly hosted with Beedie's Technology and Opera
tions Management Area) (University of Manchester)
DTSTART:20250815T170000Z
DTEND:20250815T190000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/57
DESCRIPTION:Title: F
rom Fixtures to Fairness: Analytics-Driven Decision Making in Professional
Sports\nby Ali Hassanzadeh (jointly hosted with Beedie's Technology a
nd Operations Management Area) (University of Manchester) as part of PIMS-
CORDS SFU Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAbs
tract\nNote the seminar meets at an unusual location\, WMC 4335.\n\
nTitle: From Fixtures to Fairness: Analytics-Driven Decision Making in
Professional Sports\n\nProblem definition: Professional sports leagues
may be suspended because of various reasons\, such as the recent coronavi
rus disease 2019 pandemic. A critical question that the league must addres
s when reopening is how to appropriately select a subset of the remaining
games to conclude the season in a shortened time frame. Despite the rich l
iterature on scheduling an entire season starting from a blank slate\, con
cluding an existing season is quite different. Our approach attempts to ac
hieve team rankings similar to those that would have resulted had the seas
on been played out in full. Methodology/results: We propose a data-driven
model that exploits predictive and prescriptive analytics to produce a sch
edule for the remainder of the season composed of a subset of originally s
cheduled games. Our model introduces novel rankings-based objectives withi
n a stochastic optimization model\, whose parameters are first estimated u
sing a predictive model. We introduce a deterministic equivalent reformula
tion along with a tailored Frank–Wolfe algorithm to efficiently solve ou
r problem as well as a robust counterpart based on min-max regret. We pres
ent simulation-based numerical experiments from previous National Basketba
ll Association seasons 2004–2019\, and we show that our models are compu
tationally efficient\, outperform a greedy benchmark that approximates a n
on-rankings-based scheduling policy\, and produce interpretable results. M
anagerial implications: Our data-driven decision-making framework may be u
sed to produce a shortened season with 25%–50% fewer games while still p
roducing an end-of-season ranking similar to that of the full season\, had
it been played.\n\nLink to paper: https://pubsonline.informs.org/doi/abs/
10.1287/msom.2022.0558\n\n \n\nPart II: NBA Expansion: Opportunities to
Reform the League\n\nIn this study\, we explore how the NBA could res
tructure its divisions and conferences in light of potential league expans
ion. Building on optimization models\, we consider two fairness-oriented f
ormulations: a total travel distance minimization and a Nash bargaining fr
amework that balances travel burden across teams\, as well as distribution
of media market size. Our approach evaluates realignment scenarios using
geographic clustering and provides insights into how fairness and efficien
cy can be reconciled in league design. This work highlights the value of d
ata-driven approaches in making strategic structural decisions for profess
ional sports leagues.\n
LOCATION:https://researchseminars.org/talk/SFUOR/57/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yuan Zhou (remote) (University of Kentucky)
DTSTART:20251202T233000Z
DTEND:20251203T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/58
DESCRIPTION:Title: A
ll Cyclic Group Facets Inject\nby Yuan Zhou (remote) (University of Ke
ntucky) as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture h
eld in ASB 10908.\n\nAbstract\nIn this talk\, we study cut-generating func
tions in the setting of the Gomory-Johnson group relaxations\nfor integer
programming. We address an open question: whether every facet (extreme fun
ction) for a\nfinite cyclic group relaxation injects into the space of ext
reme functions for the infinite group problem. We give a variant of the B
asu-Hildebrand-Molinaro approximation theorem [IPCO 2016] for\ncontinuous
minimal functions of the infinite group problem. Specifically\, we show th
at any piecewise\nlinear minimal function with rational breakpoints in 1/q
Z and rational values at these breakpoints can be approximated by piecewis
e linear two-slope extreme functions while preserving all function values
on\n1/qZ: a feature not guaranteed by the earlier construction. As a corol
lary\, every extreme function for the finite group problem on 1/qZ is the
restriction of a continuous piecewise linear two-slope extreme\nfunction f
or the infinite group problem\, with breakpoints on a refinement 1/(Mq)Z.
Combined with\nGomory’s master theorem\, this establishes that the infin
ite group problem indeed serves as the correct\nmaster problem for facets
of one-row group relaxations.\n\nThis is a joint work with Matthias Koeppe
.\n
LOCATION:https://researchseminars.org/talk/SFUOR/58/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Krisztina Vásárhelyi (Vancouver Coastal Health)
DTSTART:20251007T223000Z
DTEND:20251007T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/59
DESCRIPTION:Title: E
mbedded Research Program in Healthcare Operations Research: An SFU/VCH Par
tnership\nby Krisztina Vásárhelyi (Vancouver Coastal Health) as part
of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 1090
8.\n\nAbstract\nThe SFU Operations Research Clinic capstone project course
instructors have been in partnership with Vancouver Coastal Health (VCH)
since 2018. Each Spring term a cohort of students works on a problem posed
by the VCH health authority partners. This successful collaboration has g
rown into a broader partnership that fosters embedded operations research
(OR) at VCH. The Embedded Research Program is one key initiative that brin
gs two SFU faculty and two graduate students to VCH\, where they work clos
ely with health system partners on priority operational problems. This str
uctured program and the Operations Research Clinic research projects have
generated actionable findings that have informed capacity management and o
perations at VCH. This experience brought many valuable lessons about the
barriers and facilitators of embedding research into health care practice
and I will share some key insights in this talk. With the integration of t
his understanding of what works and what doesn’t\, there is potential fo
r further expansion of this SFU/VCH partnership to support health care ope
rations at VCH.\n
LOCATION:https://researchseminars.org/talk/SFUOR/59/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Peter Zhang (UBC-O hosted) (Carnegie Mellon University)
DTSTART:20251118T233000Z
DTEND:20251119T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/60
DESCRIPTION:Title: R
obust Paths: Geometry and Computation\nby Peter Zhang (UBC-O hosted) (
Carnegie Mellon University) as part of PIMS-CORDS SFU Operations Research
Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nApplying robust optimiz
ation often requires selecting an appropriate uncertainty set both in shap
e and size\, a choice that directly affects the trade-off between average-
case and worst-case performances. In practice\, this calibration is usuall
y done via trial-and-error: solving the robust optimization problem many t
imes with different uncertainty set shapes and sizes\, and examining their
performance trade-off. This process is computationally expensive and ad h
oc. In this work\, we take a principled approach to study this issue for r
obust optimization problems with linear objective functions\, convex feasi
ble regions\, and convex uncertainty sets. We introduce and study what we
define as the robust path: a set of robust solutions obtained by varying t
he uncertainty set's parameters. Our central geometric insight is that a r
obust path can be characterized as a Bregman projection of a curve (whose
geometry is defined by the uncertainty set) onto the feasible region. This
leads to a surprising discovery that the robust path can be approximated
via the trajectories of standard optimization algorithms\, such as the pro
ximal point method\, of the deterministic counterpart problem. We give a s
harp approximation error bound and show it depends on the geometry of the
feasible region and the uncertainty set. We also illustrate two special ca
ses where the approximation error is zero: the feasible region is polyhedr
ally monotone (e.g.\, a simplex feasible region under an ellipsoidal uncer
tainty set)\, or the feasible region and the uncertainty set follow a dual
relationship. We demonstrate the practical impact of this approach in two
settings: portfolio optimization and adversarial deep learning.\n
LOCATION:https://researchseminars.org/talk/SFUOR/60/
END:VEVENT
BEGIN:VEVENT
SUMMARY:14 speakers
DTSTART:20251018T155000Z
DTEND:20251018T230000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/61
DESCRIPTION:Title: W
COM 2025 at UBC-Okanagan\nby 14 speakers as part of PIMS-CORDS SFU Ope
rations Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nFor de
tails click here.\n
LOCATION:https://researchseminars.org/talk/SFUOR/61/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Daniela Lubke (remote) (University of Waterloo)
DTSTART:20251028T223000Z
DTEND:20251028T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/62
DESCRIPTION:Title: I
ntegrating Machine Scheduling and Personnel Allocation in a Large-Scale An
alytical Services Facility via Column Generation\nby Daniela Lubke (re
mote) (University of Waterloo) as part of PIMS-CORDS SFU Operations Resear
ch Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nThis work investigat
es the integration of machine scheduling and personnel allocation problems
. In\nmachine scheduling\, the goal is to find the optimal assignment of j
obs to machines within a given time\nhorizon\, considering processing time
s and machine capacities in a time-discretized model. Personnel\nallocatio
n problems aim to determine the best employee distribution while respectin
g business\,\nregulatory\, or satisfaction constraints (for example\, maxi
mum work hours for an employee in a specific\nactivity\, the duration an e
mployee can remain in one activity without rest\, or preferred workdays).
These two problems are inherently connected and should ideally be addresse
d together in a unified\nformulation\, though this is computationally dema
nding. This work introduces a column generation-based\nalgorithm designed
to find good quality solutions for an industrial-scale analytical services
facility\nwhere integrated machine scheduling and personnel allocation de
cisions are made daily. Our\ncomputational results indicate that the propo
sed approach consistently produces high-quality solutions\neven as the pro
blem size increases\, outperforming other existing methods.\n
LOCATION:https://researchseminars.org/talk/SFUOR/62/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nicholas Richardson (UBC)
DTSTART:20260120T233000Z
DTEND:20260121T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/63
DESCRIPTION:Title: O
ptimization and applications for unsupervised signal demixing\nby Nich
olas Richardson (UBC) as part of PIMS-CORDS SFU Operations Research Semina
r\n\nLecture held in ASB 10908.\n\nAbstract\nThroughout scientific and com
mercial domains\, we are often interested in separating mixed signals into
their component sources. Supervised deep learning is state-of-the-art whe
n large and well-labeled datasets can be used. But in many applications\,
large scale collection and labelling can be too impraticable\, expensive\,
or behind copyright laws. This talk will explore a number of applications
from sediment analysis\, genome sequencing\, and audio source separation
that fall into the scarce data category. We will see a few approaches I ha
ve used to model and solve these problems such as sparse feature models an
d tensor factorizations. These unsupervised learning techniques avoid a tr
aining phase and have the advantage of adapting to the specific example at
hand.\n
LOCATION:https://researchseminars.org/talk/SFUOR/63/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pengcheng Xie (remote) (Lawrence Berkeley National Laboratory)
DTSTART:20260127T233000Z
DTEND:20260128T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/64
DESCRIPTION:Title: M
odel-Based Derivative-Free Optimization with Improved Approximation\nb
y Pengcheng Xie (remote) (Lawrence Berkeley National Laboratory) as part o
f PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 10908.
\n\nAbstract\nThis talk will discuss traditional and modern approximation
techniques for (expensive) derivative-free\noptimization\, in which the ap
proximation serves as a tool for identifying the optimality of the black-b
ox objective\, rather than merely approximating the black box itself. Stat
ic (offline) strategies and dynamic (online) strategies will be discussed.
Such approximation ideas can also be naturally extended to first-order an
d higher-order methods.\n
LOCATION:https://researchseminars.org/talk/SFUOR/64/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yuriy Zinchenko (Gurobi and University of Calgary)
DTSTART:20251125T233000Z
DTEND:20251126T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/65
DESCRIPTION:Title: C
an an infeasible MIP solve itself?\nby Yuriy Zinchenko (Gurobi and Uni
versity of Calgary) as part of PIMS-CORDS SFU Operations Research Seminar\
n\nLecture held in ASB 10908.\n\nAbstract\nThe analysis of why a specific
MIP instance is infeasible formally can be reduced to computing an Irreduc
ible Infeasible Subset (IIS) of the constraints. Unlike the case of LP\, f
or MIP there is no useful duality that can be employed to facilitate such
computations. The process of determining an IIS for MIP is typically handl
ed with brute force\, e.g.\, by use of deletion filters and alike\, thus r
endering IIS determination for a MIP into a much harder computational task
. We will discuss one approach to optimizing this process and what compone
nts of this approach could make it into the newest version of Gurobi.\n
LOCATION:https://researchseminars.org/talk/SFUOR/65/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mohammad Delasay (room: WMC 5302) (Stony Brook University and Univ
ersity of Alberta)
DTSTART:20251121T200000Z
DTEND:20251121T213000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/66
DESCRIPTION:Title: D
elay Information Sharing in Two-sided Platforms\nby Mohammad Delasay (
room: WMC 5302) (Stony Brook University and University of Alberta) as part
of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in ASB 1090
8.\n\nAbstract\nWe analyze how strategic customers and providers respond
to delay information in a matching system. Using Markovian queueing models
and equilibrium analysis\, we evaluate three disclosure policies (no info
rmation\, binary\, and occupancy) and identify conditions under which each
improves match rates. The results also reveal misalignments between platf
orm-optimal choices and user preferences.\n
LOCATION:https://researchseminars.org/talk/SFUOR/66/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Youssef Diouane (remote) (Polytechnique Montréal)
DTSTART:20260224T233000Z
DTEND:20260225T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/67
DESCRIPTION:Title: D
irect-Search for Min-Max Derivative-Free Optimization\nby Youssef Diou
ane (remote) (Polytechnique Montréal) as part of PIMS-CORDS SFU Operation
s Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nRecent appli
cations in machine learning have renewed the community’s interest in min
-max\noptimization problems. While gradient-based optimization methods are
widely used to solve these\nproblems\, there exist many scenarios where s
uch techniques are not well suited\, or even not applicable\,\nparticularl
y when gradients are not accessible. In this talk\, we will investigate th
e use of direct-search methods\, which belong to a class of derivative-fre
e techniques that only require access to the objective function through an
oracle. We will present a novel direct-search method for min-max saddle-p
oint problems\, where the min and max players are updated sequentially. Th
e convergence of this algorithm will be discussed in both deterministic an
d stochastic settings. Finally\, experimental results related to robust op
timization and Generative Adversarial Networks will be presented to illust
rate how the proposed method can outperform commonly used optimization sch
emes.\n
LOCATION:https://researchseminars.org/talk/SFUOR/67/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Amzi Jeffs (remote) (Pacific Northwest National Laboratory)
DTSTART:20260324T223000Z
DTEND:20260324T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/68
DESCRIPTION:by Amzi Jeffs (remote) (Pacific Northwest National Laboratory)
as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in
ASB 10908.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/SFUOR/68/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Fredrik Odegaard (room: WMC 4335) (Ivey Business School (Universit
y of Western Ontario))
DTSTART:20260220T193000Z
DTEND:20260220T210000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/69
DESCRIPTION:Title: D
ecoding Consumer Preferences Using Attention-Based Language Models\nby
Fredrik Odegaard (room: WMC 4335) (Ivey Business School (University of We
stern Ontario)) as part of PIMS-CORDS SFU Operations Research Seminar\n\nL
ecture held in ASB 10908.\n\nAbstract\nThis paper proposes a new demand es
timation method using attention-based language models. An encoder-only lan
guage model is trained in a two-stage process to analyze the natural langu
age descriptions of used cars from a large US-based online auction marketp
lace. The approach enables semi-nonparametrically estimation for the deman
d primitives of a structural model representing the private valuations and
market size for each vehicle listing. In the first stage\, the language m
odel is fine-tuned to encode the target auction outcomes using the natural
language vehicle descriptions. In the second stage\, the trained language
model's encodings are projected into the parameter space of the structura
l model. The model's capability to conduct counterfactual analyses within
the trained market space is validated using a subsample of withheld auctio
n data\, which includes a set of unique "zero shot" instances.\n\nThis is
joint work with Joshua Foster.\n
LOCATION:https://researchseminars.org/talk/SFUOR/69/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ahmet Alacaoglu (UBC)
DTSTART:20260303T233000Z
DTEND:20260304T003000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/70
DESCRIPTION:Title: T
owards Weaker Variance Assumptions for Stochastic Optimization: A Blast Fr
om the Past\nby Ahmet Alacaoglu (UBC) as part of PIMS-CORDS SFU Operat
ions Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nIn this t
alk\, I will present some recent advances for analyzing stochastic optimiz
ation methods without the bounded variance assumption. It is well-known th
at the bounded variance assumption is violated for even the most standard
problems such as linear least squares problem. We will see that the analys
is for obtaining optimal rates of convergence under realistic variance ass
umptions builds on a connection between the classical literatures for stoc
hastic approximation and the Halpern iteration for solving fixed-point pro
blems. We will discuss the extensions to proximal algorithms for solving r
egularized problems and stochastic convex nonlinear programs\, as well as
the required ideas for getting rate guarantees on the last iterate of the
algorithm\, which is widely used in practice.\n
LOCATION:https://researchseminars.org/talk/SFUOR/70/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jeremy Chiu (SFU)
DTSTART:20260317T223000Z
DTEND:20260317T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/71
DESCRIPTION:Title: T
he burden of tuberculosis among foreign-born Canadians - estimates with dy
namic models\nby Jeremy Chiu (SFU) as part of PIMS-CORDS SFU Operation
s Research Seminar\n\nLecture held in ASB 10908.\n\nAbstract\nDespite only
comprising about a quarter of the total population of Canada\, foreign-bo
rn individuals bear about three-quarters of the burden of active tuberculo
sis (TB) cases. To investigate the impact of immigration on the burden of
TB among foreign-born Canadians\, we develop an SEIR-compartment model tha
t distinguishes between actively infected\, latently infected\, and uninfe
cted individuals. Unknown parameters are calibrated to reports on the in
cidence and prevalence of active TB in Canada. We validate our model by
comparing model computed quantities to other estimates of tuberculosis bur
den among foreign-born Canadians\, including an estimate of the prevalence
of latent TB infection (LTBI) among immigrants entering Canada. Our mod
el predicts that among the foreign-born population\, Canada will not meet
the End TB 2035 goal of reducing incidence by 90% compared to 2015\, prima
rily due to the activation of foreign-born Canadians with LTBI.\n
LOCATION:https://researchseminars.org/talk/SFUOR/71/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hongda Li (UBC-O hosted) (UBC Okanagan)
DTSTART:20260407T223000Z
DTEND:20260407T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/72
DESCRIPTION:Title: Q
uadratic Growth Gives Near Optimal Total Complexity for Inexact Accelerate
d Proximal Gradient Method\nby Hongda Li (UBC-O hosted) (UBC Okanagan)
as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture held in
ASB 10908.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/SFUOR/72/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Temesgen Abraha and Tanmaya Karmarkar (UBC-O hosted) (UBC Okanagan
)
DTSTART:20260414T223000Z
DTEND:20260414T233000Z
DTSTAMP:20260314T083629Z
UID:SFUOR/73
DESCRIPTION:by Temesgen Abraha and Tanmaya Karmarkar (UBC-O hosted) (UBC O
kanagan) as part of PIMS-CORDS SFU Operations Research Seminar\n\nLecture
held in ASB 10908.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/SFUOR/73/
END:VEVENT
END:VCALENDAR