BEGIN:VCALENDAR
VERSION:2.0
PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Sriram Sankaranarayanan (Polytechnique Montréal)
DTSTART;VALUE=DATE-TIME:20200428T180000Z
DTEND;VALUE=DATE-TIME:20200428T183500Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/1
DESCRIPTION:Title: Whe
n Nash Meets Stackelberg\nby Sriram Sankaranarayanan (Polytechnique Mo
ntréal) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Siqian Shen (University of Michigan)
DTSTART;VALUE=DATE-TIME:20200428T183500Z
DTEND;VALUE=DATE-TIME:20200428T191000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/2
DESCRIPTION:Title: New
Results and Applications of Facility Location involving Competition\, Pri
oritization\, or Decision-dependent Demand\nby Siqian Shen (University
of Michigan) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Beste Basciftci (Georgia Tech)
DTSTART;VALUE=DATE-TIME:20200505T180000Z
DTEND;VALUE=DATE-TIME:20200505T183500Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/3
DESCRIPTION:Title: Ada
ptive two-stage stochastic programming with an application to capacity exp
ansion planning\nby Beste Basciftci (Georgia Tech) as part of Discrete
Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Margarita Castro (University of Toronto)
DTSTART;VALUE=DATE-TIME:20200505T183500Z
DTEND;VALUE=DATE-TIME:20200505T191000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/4
DESCRIPTION:Title: A C
ombinatorial Cut-and-Lift Procedure with an Application to 0-1 Chance Cons
traints\nby Margarita Castro (University of Toronto) as part of Discre
te Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Simge Küçükyavuz (Northwestern)
DTSTART;VALUE=DATE-TIME:20200512T180000Z
DTEND;VALUE=DATE-TIME:20200512T183500Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/5
DESCRIPTION:Title: Dis
tributionally Robust Chance-Constrained Programs with Right-Hand Side Unce
rtainty under Wasserstein Ambiguity\nby Simge Küçükyavuz (Northwest
ern) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hussein Hazimeh (MIT)
DTSTART;VALUE=DATE-TIME:20200512T183500Z
DTEND;VALUE=DATE-TIME:20200512T191000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/6
DESCRIPTION:Title: Spa
rse regression at scale: branch-and-bound rooted in first-order optimizati
on\nby Hussein Hazimeh (MIT) as part of Discrete Optimization Talks\n\
nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hadi Charkhgard (USF)
DTSTART;VALUE=DATE-TIME:20200526T180000Z
DTEND;VALUE=DATE-TIME:20200526T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/7
DESCRIPTION:Title: The
magic of Nash social welfare in optimization: Do not sum\, just multiply!
\nby Hadi Charkhgard (USF) as part of Discrete Optimization Talks\n\nA
bstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Phebe Vayanos (USC)
DTSTART;VALUE=DATE-TIME:20200526T183000Z
DTEND;VALUE=DATE-TIME:20200526T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/8
DESCRIPTION:Title: Act
ive preference elicitation via adjustable robust optimization\nby Pheb
e Vayanos (USC) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hamed Rahimian (Northwestern University)
DTSTART;VALUE=DATE-TIME:20200421T180000Z
DTEND;VALUE=DATE-TIME:20200421T183500Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/9
DESCRIPTION:Title: A M
odel of Supply-Chain Decisions for Resource Sharing with an Application to
Ventilator Allocation to Combat COVID-19\nby Hamed Rahimian (Northwes
tern University) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pierre Le Bodic (Monash University)
DTSTART;VALUE=DATE-TIME:20200421T183500Z
DTEND;VALUE=DATE-TIME:20200421T193000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/10
DESCRIPTION:Title: Es
timating the Size of Branch-and-Bound Trees\nby Pierre Le Bodic (Monas
h University) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Weijun Xie (Virginia Tech)
DTSTART;VALUE=DATE-TIME:20200602T180000Z
DTEND;VALUE=DATE-TIME:20200602T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/11
DESCRIPTION:Title: Be
st Submatrix Selection: Strong Formulations and Approximation Algorithms\nby Weijun Xie (Virginia Tech) as part of Discrete Optimization Talks\n
\n\nAbstract\nMany interesting machine learning and data analytics problem
s involve selecting the most informative principal submatix of of a prespe
cified size from a covariance matrix\, such as maximum entropy sampling pr
oblem\, experimental design\, sparse PCA. Although directly formulating th
ese problems into mathematical programs is difficult\, we explore the Chol
esky factorization of the original covariance matrix and recast the proble
ms as mixed integer programs (MIPs). We also show that (i) these new MIPs
usually have tight continuous relaxation bounds\, and (ii) by constructing
dual solutions\, we can prove approximation bounds of the local search al
gorithm. Our numerical experiments demonstrate that these approximation al
gorithms can efficiently solve medium-sized and large-scale instances to n
ear-optimality.\n
LOCATION:https://researchseminars.org/talk/DOTs/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Andrés Gómez (USC)
DTSTART;VALUE=DATE-TIME:20200602T183000Z
DTEND;VALUE=DATE-TIME:20200602T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/12
DESCRIPTION:Title: Ou
tlier detection in time series via mixed-integer conic quadratic optimizat
ion\nby Andrés Gómez (USC) as part of Discrete Optimization Talks\n\
n\nAbstract\nWe consider the problem of estimating the true values of a Wi
ener process given noisy observations corrupted by outliers. The problem c
onsidered is closely related to the Trimmed Least Squares estimation probl
em\, a robust estimation procedure well-studied from a statistical standpo
int but poorly understood from an optimization perspective. In this paper
we show how to improve existing mixed-integer quadratic optimization formu
lations for this problem. Specifically\, we convexify the existing formula
tions via lifting\, deriving new mixed-integer conic quadratic reformulati
ons. The proposed reformulations are stronger and substantially faster whe
n used with current mixed-integer optimization solvers. In our experiments
\, solution times are improved by at least two orders-of-magnitude.\n
LOCATION:https://researchseminars.org/talk/DOTs/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Axel Parmentier (École des Ponts ParisTech)
DTSTART;VALUE=DATE-TIME:20200616T180000Z
DTEND;VALUE=DATE-TIME:20200616T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/13
DESCRIPTION:Title: Le
arning to approximate industrial problems by operations research classic p
roblems\nby Axel Parmentier (École des Ponts ParisTech) as part of Di
screte Optimization Talks\n\n\nAbstract\nPractitioners of operations resea
rch often consider difficult variants of well-known optimization problems\
, and struggle to find a good algorithm for their variants while decades o
f research have produced highly efficient algorithms for the well-known pr
oblems. We introduce a "machine learning for operations research" paradigm
to build efficient heuristics for such variants of well-known problems. I
f we call the difficult problem of interest the hard problem\, and the wel
l known one the easy problem\, we can describe our paradigm as follows. Fi
rst\, use a machine learning predictor to turn an instance of the hard pro
blem into an instance of the easy one\, then solve the instance of the eas
y problem\, and finally retrieve a solution of the hard problem from the s
olution of the easy one. Using this paradigm requires to learn the predict
or that transforms an instance of the hard problem into an instance of the
easy one. We introduce two structured learning approaches to learn this p
redictor\, and illustrate our paradigm and learning methodologies on sever
al scheduling and path problems.\n
LOCATION:https://researchseminars.org/talk/DOTs/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dolores Romero Morales (Copenhagen Business School)
DTSTART;VALUE=DATE-TIME:20200616T183000Z
DTEND;VALUE=DATE-TIME:20200616T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/14
DESCRIPTION:Title: On
Enhancing the Interpretability of Data Science Models via Dimensionality
Reduction\nby Dolores Romero Morales (Copenhagen Business School) as p
art of Discrete Optimization Talks\n\n\nAbstract\nData Science aims to dev
elop models that extract knowledge from complex data to aid Data Driven De
cision Making. There is a growing literature on enhancing the interpretabi
lity of Data Science methods. Interpretability is desirable for non-expert
s\; it is required by regulators for models aiding\, for instance\, credit
scoring\; and since 2018 the EU has extended this requirement by imposing
the so-called right-to-explanation. Mathematical Optimization has shown a
crucial role when striking a balance between interpretability and accurac
y\, having LASSO as one of the main exponents. In this presentation\, we w
ill navigate through some novel dimensionality reduction techniques embedd
ed in the construction of data science models\, to enhance their interpret
ability.\n
LOCATION:https://researchseminars.org/talk/DOTs/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ricardo Fukasawa (University of Waterloo)
DTSTART;VALUE=DATE-TIME:20200623T180000Z
DTEND;VALUE=DATE-TIME:20200623T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/15
DESCRIPTION:Title: En
forcing non-anticipativity in a two-stage stochastic program for schedulin
g with endogenous uncertainties\nby Ricardo Fukasawa (University of Wa
terloo) as part of Discrete Optimization Talks\n\n\nAbstract\nMost of the
research in two-stage stochastic programs has focused on the case of exoge
nous uncertainties\, that is\, uncertainties that are not influenced by an
y decision that the stochastic program takes. In this talk I will present
work on a scheduling problem arising in the analytical services sector\, f
or which the uncertainty is endogenous. In particular\, the time of realiz
ation of uncertainty is defined by the decisions taken by the stochastic p
rogram. In such a context\, enforcing non-anticipativity becomes a challen
ging proposition\, since one does not know a priori when uncertainty is re
alized. \n\nThe typical approach for these types of problems has been to i
ntroduce binary variables that indicate when exactly uncertainty gets real
ized. While these binary variables are useful to more easily enforce non-a
nticipativity constraints\, they greatly complicate the solution to such m
odels and much of the research focus has been to develop better algorithms
to handle such binary variables and decompose such problem.\n\nIn this wo
rk\, we present a model for scheduling services in the analytical services
sector\, where the uncertainty is in the analysis results. The developed
model is based on a flow formulation of a time discretized version of the
problem. Due to this particular structure\, a careful choice of parameters
allows us to develop a two-stage model that enforces non-anticipativity w
ithout the addition of any new binary variables. We will discuss advantage
s and drawbacks of such approach and potential future directions of resear
ch. \n\nThis is based on joint work with Kavitha Menon and Luis Ricardez-
Sandoval.\n
LOCATION:https://researchseminars.org/talk/DOTs/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Austin Buchanan (Oklahoma State University)
DTSTART;VALUE=DATE-TIME:20200623T183000Z
DTEND;VALUE=DATE-TIME:20200623T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/16
DESCRIPTION:Title: Im
posing contiguity constraints in political districting models\nby Aust
in Buchanan (Oklahoma State University) as part of Discrete Optimization T
alks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Timo Berthold (FICO)
DTSTART;VALUE=DATE-TIME:20200630T180000Z
DTEND;VALUE=DATE-TIME:20200630T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/17
DESCRIPTION:Title: Le
arning to Scale\nby Timo Berthold (FICO) as part of Discrete Optimizat
ion Talks\n\n\nAbstract\nScaling is a widely used preconditioning techniqu
e\, used to reduce error propagation and thereby improve the numerical beh
avior of an algorithm.\nFor numerically challenging mixed-integer programs
(MIPs)\, as they appear in many practical applications\, having an effici
ent scaling method in place often makes the difference whether the MIP's L
P relaxations can be solved gracefully or not.\nThere are two scaling meth
ods which are commonly used: Standard scaling and Curtis-Reid scaling.\nTh
e latter often\, but not always\, leads to a more robust solution process\
, but also to longer solution times.\nWe introduce a method to automatical
ly choose between the two scaling variants by predicting which one will le
ad to fewer numerical issues.\nIt turns out that this not only reduces var
ious types of numerical errors\, but is also performance-neutral for MIPs
and improves performance on LPs.\n
LOCATION:https://researchseminars.org/talk/DOTs/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Amitabh Basu (Johns Hopkins University)
DTSTART;VALUE=DATE-TIME:20200707T180000Z
DTEND;VALUE=DATE-TIME:20200707T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/18
DESCRIPTION:Title: Pr
ovable complexity bounds for integer programming algorithms\nby Amitab
h Basu (Johns Hopkins University) as part of Discrete Optimization Talks\n
\n\nAbstract\nWe discuss the complexity of the two main ingredients in int
eger optimization algorithms: cutting planes and branch-and-bound. We prov
e upper and lower bounds on the efficiency of these algorithms\, when effi
ciency is measured in terms of complexity of the LPs that are solved. More
precisely\, we focus on the sparsity of the LPs and the number of LPs as
measures of complexity. Some connections with mathematical logic and proof
complexity will also be discussed.\n
LOCATION:https://researchseminars.org/talk/DOTs/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Aida Khajavirad (Rutgers University)
DTSTART;VALUE=DATE-TIME:20200707T183000Z
DTEND;VALUE=DATE-TIME:20200707T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/19
DESCRIPTION:Title: Th
e ratio-cut polytope and K-means clustering\nby Aida Khajavirad (Rutge
rs University) as part of Discrete Optimization Talks\n\n\nAbstract\nWe in
troduce the ratio-cut polytope defined as the convex hull of ratio-cut vec
tors corresponding to all partitions of $n$ points in $R^m$ into at most $
K$ clusters. This polytope is closely related to the convex hull of the fe
asible region of a number of clustering problems such as K-means clusterin
g and spectral clustering. We study the facial structure of the ratio-cut
polytope and derive several types of facet-defining inequalities. We then
consider the problem of K-means clustering and introduce a novel linear pr
ogramming (LP) relaxation for it. Subsequently\, we focus on the case of t
wo clusters and derive sufficient conditions under which the proposed LP r
elaxation recovers the underlying clusters exactly. Namely\, we consider t
he stochastic ball model\, a popular generative model for K-means clusteri
ng\, and we show that if the separation distance between cluster centers s
atisfies $\\Delta > 1+\\sqrt 3$\, then the LP relaxation recovers the plan
ted clusters with high probability. This is a major improvement over the o
nly existing recovery guarantee for an LP relaxation of K-means clustering
stating that recovery is possible with high probability if and only if $\
\Delta > 4$. Our numerical experiments indicate that the proposed LP relax
ation significantly outperforms a popular semidefinite programming relaxat
ion in recovering the planted clusters.\n
LOCATION:https://researchseminars.org/talk/DOTs/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jean Pauphilet (London Business School)
DTSTART;VALUE=DATE-TIME:20200630T183000Z
DTEND;VALUE=DATE-TIME:20200630T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/20
DESCRIPTION:Title: A
Unified Approach to Mixed-Integer Optimization: Nonlinear Formulations and
Scalable Algorithms\nby Jean Pauphilet (London Business School) as pa
rt of Discrete Optimization Talks\n\n\nAbstract\nWe propose a unified fram
ework to address a family of classical mixed-integer optimization problems
with semicontinuous decision variables\, including network design\, facil
ity location\, unit commitment\, sparse portfolio selection\, binary quadr
atic optimization\, sparse principal component analysis\, and sparse learn
ing problems. These problems exhibit logical relationships between continu
ous and discrete variables\, which are usually reformulated linearly using
a big-M formulation. In this work\, we challenge this longstanding modeli
ng practice and express the logical constraints in a non-linear way. By im
posing a regularization condition\, we reformulate these problems as conve
x binary optimization problems\, which are solvable using an outer-approxi
mation procedure. Numerically\, we establish that a general-purpose strate
gy\, combining cutting-plane\, first-order\, and local search methods\, so
lves these problems faster and at a larger scale than MICO solvers. For in
stance\, our approach successfully solves network design problems with 100
s of nodes and provides solutions up to 40% better.\n
LOCATION:https://researchseminars.org/talk/DOTs/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jannis Kurtz (University of Siegen)
DTSTART;VALUE=DATE-TIME:20200714T180000Z
DTEND;VALUE=DATE-TIME:20200714T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/21
DESCRIPTION:Title: Di
screte Optimization Methods for Group Model Selection in Compressed Sensin
g\nby Jannis Kurtz (University of Siegen) as part of Discrete Optimiza
tion Talks\n\n\nAbstract\nWe study the problem of signal recovery for grou
p models. More precisely for a given set of groups\, each containing a sma
ll subset of indices\, and for given linear sketches of the true signal ve
ctor which is known to be group-sparse in the sense that its support is co
ntained in the union of a small number of these groups\, we study algorith
ms which successfully recover the true signal just by the knowledge of its
linear sketches. We consider two versions of the classical Iterative Hard
Thresholding algorithm (IHT). The classical version iteratively calculate
s the exact projection of a vector onto the group model\, while the approx
imate version (AM-IHT) uses a head- and a tail-approximation iteratively.
We apply both variants to group models and analyse the two cases where the
sensing matrix is a Gaussian matrix and a model expander matrix.\n\nTo so
lve the exact projection problem on the group model\, which is known to be
equivalent to the maximum weight coverage problem\, we use discrete optim
ization methods based on dynamic programming and Benders' Decomposition. T
he head- and tail-approximations are derived by a classical greedy-method
and LP-rounding\, respectively.\n
LOCATION:https://researchseminars.org/talk/DOTs/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Merve Bodur (University of Toronto)
DTSTART;VALUE=DATE-TIME:20210129T180000Z
DTEND;VALUE=DATE-TIME:20210129T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/22
DESCRIPTION:Title: In
verse Mixed Integer Optimization: Polyhedral Insights and Trust Region Met
hods\nby Merve Bodur (University of Toronto) as part of Discrete Optim
ization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ward Romeijnder (University of Groningen)
DTSTART;VALUE=DATE-TIME:20210129T183000Z
DTEND;VALUE=DATE-TIME:20210129T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/23
DESCRIPTION:Title: A
Converging Benders’ Decomposition Algorithm for Two-Stage Mixed-Integer
Recourse Models\nby Ward Romeijnder (University of Groningen) as part
of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thibaut Vidal (Polytechnique Montréal)
DTSTART;VALUE=DATE-TIME:20210226T180000Z
DTEND;VALUE=DATE-TIME:20210226T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/24
DESCRIPTION:Title: Bo
rn-Again Tree Ensembles: Seeing the Forest for the Trees\nby Thibaut
Vidal (Polytechnique Montréal) as part of Discrete Optimization Talks\n\n
Abstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marianna De Santis (Sapienza Università di Roma)
DTSTART;VALUE=DATE-TIME:20210226T183000Z
DTEND;VALUE=DATE-TIME:20210226T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/25
DESCRIPTION:Title: Ex
act approaches for multiobjective mixed integer nonlinear programming prob
lems\nby Marianna De Santis (Sapienza Università di Roma) as part of
Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yiling Zhang (University of Minnesota)
DTSTART;VALUE=DATE-TIME:20210326T173000Z
DTEND;VALUE=DATE-TIME:20210326T180000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/26
DESCRIPTION:Title: Bu
ilding Load Control using Distributionally Robust Binary Chance-Constraine
d Programs with Right-Hand Side Uncertainty and the Adjustable Variants\nby Yiling Zhang (University of Minnesota) as part of Discrete Optimizat
ion Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Robert Hildenbrand (Virginia Tech)
DTSTART;VALUE=DATE-TIME:20210430T170000Z
DTEND;VALUE=DATE-TIME:20210430T173000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/27
DESCRIPTION:Title: Co
mpact mixed-integer programming relaxations in quadratic optimization\
nby Robert Hildenbrand (Virginia Tech) as part of Discrete Optimization Ta
lks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ruth Misener (Imperial College)
DTSTART;VALUE=DATE-TIME:20210430T173000Z
DTEND;VALUE=DATE-TIME:20210430T180000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/28
DESCRIPTION:Title: Pa
rtial Lasserre Relaxation for Sparse Max Cut\nby Ruth Misener (Imperia
l College) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/28/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Andrew Trapp (Worcester Polytechnic Institute)
DTSTART;VALUE=DATE-TIME:20210326T173000Z
DTEND;VALUE=DATE-TIME:20210326T180000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/29
DESCRIPTION:Title: A
Comparative Study of Stability Representations for Solving Many-to-One Mat
ching Problems with Ties and Incomplete Lists via Integer Optimization
\nby Andrew Trapp (Worcester Polytechnic Institute) as part of Discrete Op
timization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yuri Faenza (Columbia University)
DTSTART;VALUE=DATE-TIME:20211015T170000Z
DTEND;VALUE=DATE-TIME:20211015T173000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/30
DESCRIPTION:Title: St
able matchings\, lattices\, and polytopes\nby Yuri Faenza (Columbia Un
iversity) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/30/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Margarida Carvalho (Polytechnique Montréal)
DTSTART;VALUE=DATE-TIME:20211015T173000Z
DTEND;VALUE=DATE-TIME:20211015T180000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/31
DESCRIPTION:Title: Se
quential matching markets\nby Margarida Carvalho (Polytechnique Montr
éal) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marc Pfetsch (TU Darmstadt)
DTSTART;VALUE=DATE-TIME:20220128T180000Z
DTEND;VALUE=DATE-TIME:20220128T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/32
DESCRIPTION:Title: Pr
esolving for Mixed-Integer Semidefinite Optimization\nby Marc Pfetsch
(TU Darmstadt) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Victor Zavala (University of Wisconsin-Madison)
DTSTART;VALUE=DATE-TIME:20220128T183000Z
DTEND;VALUE=DATE-TIME:20220128T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/33
DESCRIPTION:Title: So
lution of Large-Scale Supply Chain Models using Graph Sampling & Coarsenin
g\nby Victor Zavala (University of Wisconsin-Madison) as part of Discr
ete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Carla Michini (University of Wisconsin-Madison)
DTSTART;VALUE=DATE-TIME:20211119T180000Z
DTEND;VALUE=DATE-TIME:20211119T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/34
DESCRIPTION:Title: Th
e Price of Anarchy in Series-Parallel Network Congestion Games\nby Car
la Michini (University of Wisconsin-Madison) as part of Discrete Optimizat
ion Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/34/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Christian Tjandraatmadja (Google)
DTSTART;VALUE=DATE-TIME:20211119T183000Z
DTEND;VALUE=DATE-TIME:20211119T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/35
DESCRIPTION:Title: Co
nstrained Discrete Black-Box Optimization using Mixed-Integer Programming<
/a>\nby Christian Tjandraatmadja (Google) as part of Discrete Optimization
Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/35/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ivana Ljubic (ESSEC Business School of Paris)
DTSTART;VALUE=DATE-TIME:20211210T180000Z
DTEND;VALUE=DATE-TIME:20211210T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/36
DESCRIPTION:Title: Lo
wer Bounds for Ramsey Numbers on Circulant Graphs: A Bilevel Optimization
Approach\nby Ivana Ljubic (ESSEC Business School of Paris) as part of
Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Danial Davarnia (Iowa State University)
DTSTART;VALUE=DATE-TIME:20220225T180000Z
DTEND;VALUE=DATE-TIME:20220225T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/37
DESCRIPTION:Title: Re
ctangular decomposition of mixed integer programs via decision diagrams wi
th application to unit commitment\nby Danial Davarnia (Iowa State Univ
ersity) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/37/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Michael Poss (LIRMM)
DTSTART;VALUE=DATE-TIME:20220225T183000Z
DTEND;VALUE=DATE-TIME:20220225T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/38
DESCRIPTION:Title: Op
timization problems in graphs with locational uncertainty\nby Michael
Poss (LIRMM) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/38/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jorge Sefair (Arizona State University)
DTSTART;VALUE=DATE-TIME:20220325T170000Z
DTEND;VALUE=DATE-TIME:20220325T173000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/39
DESCRIPTION:Title: Co
ntinuous location models in the presence of obstacles: an application to w
ireless sensor networks\nby Jorge Sefair (Arizona State University) as
part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/39/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alexandra Newman (Colorado School of Mines)
DTSTART;VALUE=DATE-TIME:20220325T173000Z
DTEND;VALUE=DATE-TIME:20220325T180000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/40
DESCRIPTION:Title: Op
timizing Design and Operations of Concentrated Solar Power Plants\nby
Alexandra Newman (Colorado School of Mines) as part of Discrete Optimizati
on Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/40/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Swati Gupta (Georgia Tech)
DTSTART;VALUE=DATE-TIME:20220429T170000Z
DTEND;VALUE=DATE-TIME:20220429T173000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/41
DESCRIPTION:Title: Re
using Combinatorial Structure for Projections over Submodular Polytopes\nby Swati Gupta (Georgia Tech) as part of Discrete Optimization Talks\n\
nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/41/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Emma Frejinger (Université de Montréal)
DTSTART;VALUE=DATE-TIME:20220429T173000Z
DTEND;VALUE=DATE-TIME:20220429T180000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/42
DESCRIPTION:Title: Fa
st Heuristic L-Shaped Method Through Machine Learning\nby Emma Frejing
er (Université de Montréal) as part of Discrete Optimization Talks\n\nAb
stract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/42/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Laura Sanità
DTSTART;VALUE=DATE-TIME:20220923T170000Z
DTEND;VALUE=DATE-TIME:20220923T173000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/43
DESCRIPTION:by Laura Sanità as part of Discrete Optimization Talks\n\nAbs
tract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/43/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Elina Rönnberg
DTSTART;VALUE=DATE-TIME:20220923T173000Z
DTEND;VALUE=DATE-TIME:20220923T180000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/44
DESCRIPTION:Title: In
teger programming column generation: Accelerating branch-and-price for set
covering\, packing\, and partitioning problems\nby Elina Rönnberg as
part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/44/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anirudh Subramanyam
DTSTART;VALUE=DATE-TIME:20221028T170000Z
DTEND;VALUE=DATE-TIME:20221028T173000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/45
DESCRIPTION:by Anirudh Subramanyam as part of Discrete Optimization Talks\
n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/45/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sophie Huiberts
DTSTART;VALUE=DATE-TIME:20221028T173000Z
DTEND;VALUE=DATE-TIME:20221028T180000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/46
DESCRIPTION:by Sophie Huiberts as part of Discrete Optimization Talks\n\nA
bstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/46/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Joseph Paat
DTSTART;VALUE=DATE-TIME:20221118T180000Z
DTEND;VALUE=DATE-TIME:20221118T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/47
DESCRIPTION:by Joseph Paat as part of Discrete Optimization Talks\n\nAbstr
act: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/47/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Christopher Hojny
DTSTART;VALUE=DATE-TIME:20221118T183000Z
DTEND;VALUE=DATE-TIME:20221118T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/48
DESCRIPTION:by Christopher Hojny as part of Discrete Optimization Talks\n\
nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/48/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Claudio Contardo
DTSTART;VALUE=DATE-TIME:20221209T180000Z
DTEND;VALUE=DATE-TIME:20221209T183000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/49
DESCRIPTION:Title: MI
P-based branch-and-bound for the discrete ordered median problem\nby C
laudio Contardo as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/49/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anand Subramanian
DTSTART;VALUE=DATE-TIME:20221209T183000Z
DTEND;VALUE=DATE-TIME:20221209T190000Z
DTSTAMP;VALUE=DATE-TIME:20220927T041417Z
UID:DOTs/50
DESCRIPTION:by Anand Subramanian as part of Discrete Optimization Talks\n\
nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/DOTs/50/
END:VEVENT
END:VCALENDAR