BEGIN:VCALENDAR
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PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Sriram Sankaranarayanan (Polytechnique Montréal)
DTSTART:20200428T180000Z
DTEND:20200428T183500Z
DTSTAMP:20260404T111102Z
UID:DOTs/1
DESCRIPTION:Title: When Nash Meets Stackelberg\nby Sriram Sankaranarayanan (Polytechn
ique Montréal) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Siqian Shen (University of Michigan)
DTSTART:20200428T183500Z
DTEND:20200428T191000Z
DTSTAMP:20260404T111102Z
UID:DOTs/2
DESCRIPTION:Title: New Results and Applications of Facility Location involving Competitio
n\, Prioritization\, or Decision-dependent Demand\nby Siqian Shen (Uni
versity of Michigan) as part of Discrete Optimization Talks\n\nAbstract: T
BA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Beste Basciftci (Georgia Tech)
DTSTART:20200505T180000Z
DTEND:20200505T183500Z
DTSTAMP:20260404T111102Z
UID:DOTs/3
DESCRIPTION:Title: Adaptive two-stage stochastic programming with an application to capac
ity expansion planning\nby Beste Basciftci (Georgia Tech) as part of D
iscrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Margarita Castro (University of Toronto)
DTSTART:20200505T183500Z
DTEND:20200505T191000Z
DTSTAMP:20260404T111102Z
UID:DOTs/4
DESCRIPTION:Title: A Combinatorial Cut-and-Lift Procedure with an Application to 0-1 Chan
ce Constraints\nby Margarita Castro (University of Toronto) as part of
Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Simge Küçükyavuz (Northwestern)
DTSTART:20200512T180000Z
DTEND:20200512T183500Z
DTSTAMP:20260404T111102Z
UID:DOTs/5
DESCRIPTION:Title: Distributionally Robust Chance-Constrained Programs with Right-Hand Si
de Uncertainty under Wasserstein Ambiguity\nby Simge Küçükyavuz (No
rthwestern) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hussein Hazimeh (MIT)
DTSTART:20200512T183500Z
DTEND:20200512T191000Z
DTSTAMP:20260404T111102Z
UID:DOTs/6
DESCRIPTION:Title: Sparse regression at scale: branch-and-bound rooted in first-order opt
imization\nby Hussein Hazimeh (MIT) as part of Discrete Optimization T
alks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hadi Charkhgard (USF)
DTSTART:20200526T180000Z
DTEND:20200526T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/7
DESCRIPTION:Title: The magic of Nash social welfare in optimization: Do not sum\, just mu
ltiply!\nby Hadi Charkhgard (USF) as part of Discrete Optimization Tal
ks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Phebe Vayanos (USC)
DTSTART:20200526T183000Z
DTEND:20200526T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/8
DESCRIPTION:Title: Active preference elicitation via adjustable robust optimization\n
by Phebe Vayanos (USC) as part of Discrete Optimization Talks\n\nAbstract:
TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hamed Rahimian (Northwestern University)
DTSTART:20200421T180000Z
DTEND:20200421T183500Z
DTSTAMP:20260404T111102Z
UID:DOTs/9
DESCRIPTION:Title: A Model of Supply-Chain Decisions for Resource Sharing with an Applica
tion to Ventilator Allocation to Combat COVID-19\nby Hamed Rahimian (N
orthwestern University) as part of Discrete Optimization Talks\n\nAbstract
: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pierre Le Bodic (Monash University)
DTSTART:20200421T183500Z
DTEND:20200421T193000Z
DTSTAMP:20260404T111102Z
UID:DOTs/10
DESCRIPTION:Title: Estimating the Size of Branch-and-Bound Trees\nby Pierre Le Bodic
(Monash University) as part of Discrete Optimization Talks\n\nAbstract: T
BA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Weijun Xie (Virginia Tech)
DTSTART:20200602T180000Z
DTEND:20200602T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/11
DESCRIPTION:Title: Best Submatrix Selection: Strong Formulations and Approximation Algor
ithms\nby Weijun Xie (Virginia Tech) as part of Discrete Optimization
Talks\n\n\nAbstract\nMany interesting machine learning and data analytics
problems involve selecting the most informative principal submatix of of a
prespecified size from a covariance matrix\, such as maximum entropy samp
ling problem\, experimental design\, sparse PCA. Although directly formula
ting these problems into mathematical programs is difficult\, we explore t
he Cholesky factorization of the original covariance matrix and recast the
problems as mixed integer programs (MIPs). We also show that (i) these ne
w MIPs usually have tight continuous relaxation bounds\, and (ii) by const
ructing dual solutions\, we can prove approximation bounds of the local se
arch algorithm. Our numerical experiments demonstrate that these approxima
tion algorithms can efficiently solve medium-sized and large-scale instanc
es to near-optimality.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Andrés Gómez (USC)
DTSTART:20200602T183000Z
DTEND:20200602T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/12
DESCRIPTION:Title: Outlier detection in time series via mixed-integer conic quadratic op
timization\nby Andrés Gómez (USC) as part of Discrete Optimization T
alks\n\n\nAbstract\nWe consider the problem of estimating the true values
of a Wiener process given noisy observations corrupted by outliers. The pr
oblem considered is closely related to the Trimmed Least Squares estimatio
n problem\, a robust estimation procedure well-studied from a statistical
standpoint but poorly understood from an optimization perspective. In this
paper we show how to improve existing mixed-integer quadratic optimizatio
n formulations for this problem. Specifically\, we convexify the existing
formulations via lifting\, deriving new mixed-integer conic quadratic refo
rmulations. The proposed reformulations are stronger and substantially fas
ter when used with current mixed-integer optimization solvers. In our expe
riments\, solution times are improved by at least two orders-of-magnitude.
\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Axel Parmentier (École des Ponts ParisTech)
DTSTART:20200616T180000Z
DTEND:20200616T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/13
DESCRIPTION:Title: Learning to approximate industrial problems by operations research cl
assic problems\nby Axel Parmentier (École des Ponts ParisTech) as par
t of Discrete Optimization Talks\n\n\nAbstract\nPractitioners of operation
s research often consider difficult variants of well-known optimization pr
oblems\, and struggle to find a good algorithm for their variants while de
cades of research have produced highly efficient algorithms for the well-k
nown problems. We introduce a "machine learning for operations research" p
aradigm to build efficient heuristics for such variants of well-known prob
lems. If we call the difficult problem of interest the hard problem\, and
the well known one the easy problem\, we can describe our paradigm as foll
ows. First\, use a machine learning predictor to turn an instance of the h
ard problem into an instance of the easy one\, then solve the instance of
the easy problem\, and finally retrieve a solution of the hard problem fro
m the solution of the easy one. Using this paradigm requires to learn the
predictor that transforms an instance of the hard problem into an instance
of the easy one. We introduce two structured learning approaches to learn
this predictor\, and illustrate our paradigm and learning methodologies o
n several scheduling and path problems.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dolores Romero Morales (Copenhagen Business School)
DTSTART:20200616T183000Z
DTEND:20200616T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/14
DESCRIPTION:Title: On Enhancing the Interpretability of Data Science Models via Dimensio
nality Reduction\nby Dolores Romero Morales (Copenhagen Business Schoo
l) as part of Discrete Optimization Talks\n\n\nAbstract\nData Science aims
to develop models that extract knowledge from complex data to aid Data Dr
iven Decision Making. There is a growing literature on enhancing the inter
pretability of Data Science methods. Interpretability is desirable for non
-experts\; it is required by regulators for models aiding\, for instance\,
credit scoring\; and since 2018 the EU has extended this requirement by i
mposing the so-called right-to-explanation. Mathematical Optimization has
shown a crucial role when striking a balance between interpretability and
accuracy\, having LASSO as one of the main exponents. In this presentation
\, we will navigate through some novel dimensionality reduction techniques
embedded in the construction of data science models\, to enhance their in
terpretability.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ricardo Fukasawa (University of Waterloo)
DTSTART:20200623T180000Z
DTEND:20200623T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/15
DESCRIPTION:Title: Enforcing non-anticipativity in a two-stage stochastic program for sc
heduling with endogenous uncertainties\nby Ricardo Fukasawa (Universit
y of Waterloo) as part of Discrete Optimization Talks\n\n\nAbstract\nMost
of the research in two-stage stochastic programs has focused on the case o
f exogenous uncertainties\, that is\, uncertainties that are not influence
d by any decision that the stochastic program takes. In this talk I will p
resent work on a scheduling problem arising in the analytical services sec
tor\, for which the uncertainty is endogenous. In particular\, the time of
realization of uncertainty is defined by the decisions taken by the stoch
astic program. In such a context\, enforcing non-anticipativity becomes a
challenging proposition\, since one does not know a priori when uncertaint
y is realized. \n\nThe typical approach for these types of problems has be
en to introduce binary variables that indicate when exactly uncertainty ge
ts realized. While these binary variables are useful to more easily enforc
e non-anticipativity constraints\, they greatly complicate the solution to
such models and much of the research focus has been to develop better alg
orithms to handle such binary variables and decompose such problem.\n\nIn
this work\, we present a model for scheduling services in the analytical s
ervices sector\, where the uncertainty is in the analysis results. The dev
eloped model is based on a flow formulation of a time discretized version
of the problem. Due to this particular structure\, a careful choice of par
ameters allows us to develop a two-stage model that enforces non-anticipat
ivity without the addition of any new binary variables. We will discuss ad
vantages and drawbacks of such approach and potential future directions of
research. \n\nThis is based on joint work with Kavitha Menon and Luis Ri
cardez-Sandoval.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Austin Buchanan (Oklahoma State University)
DTSTART:20200623T183000Z
DTEND:20200623T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/16
DESCRIPTION:Title: Imposing contiguity constraints in political districting models\n
by Austin Buchanan (Oklahoma State University) as part of Discrete Optimiz
ation Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Timo Berthold (FICO)
DTSTART:20200630T180000Z
DTEND:20200630T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/17
DESCRIPTION:Title: Learning to Scale\nby Timo Berthold (FICO) as part of Discrete Op
timization Talks\n\n\nAbstract\nScaling is a widely used preconditioning t
echnique\, used to reduce error propagation and thereby improve the numeri
cal behavior of an algorithm.\nFor numerically challenging mixed-integer p
rograms (MIPs)\, as they appear in many practical applications\, having an
efficient scaling method in place often makes the difference whether the
MIP's LP relaxations can be solved gracefully or not.\nThere are two scali
ng methods which are commonly used: Standard scaling and Curtis-Reid scali
ng.\nThe latter often\, but not always\, leads to a more robust solution p
rocess\, but also to longer solution times.\nWe introduce a method to auto
matically choose between the two scaling variants by predicting which one
will lead to fewer numerical issues.\nIt turns out that this not only redu
ces various types of numerical errors\, but is also performance-neutral fo
r MIPs and improves performance on LPs.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Amitabh Basu (Johns Hopkins University)
DTSTART:20200707T180000Z
DTEND:20200707T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/18
DESCRIPTION:Title: Provable complexity bounds for integer programming algorithms\nby
Amitabh Basu (Johns Hopkins University) as part of Discrete Optimization
Talks\n\n\nAbstract\nWe discuss the complexity of the two main ingredients
in integer optimization algorithms: cutting planes and branch-and-bound.
We prove upper and lower bounds on the efficiency of these algorithms\, wh
en efficiency is measured in terms of complexity of the LPs that are solve
d. More precisely\, we focus on the sparsity of the LPs and the number of
LPs as measures of complexity. Some connections with mathematical logic an
d proof complexity will also be discussed.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Aida Khajavirad (Rutgers University)
DTSTART:20200707T183000Z
DTEND:20200707T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/19
DESCRIPTION:Title: The ratio-cut polytope and K-means clustering\nby Aida Khajavirad
(Rutgers University) as part of Discrete Optimization Talks\n\n\nAbstract
\nWe introduce the ratio-cut polytope defined as the convex hull of ratio-
cut vectors 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 feasible region of a number of clustering problems such as K-means cl
ustering and spectral clustering. We study the facial structure of the rat
io-cut polytope and derive several types of facet-defining inequalities. W
e then consider the problem of K-means clustering and introduce a novel li
near programming (LP) relaxation for it. Subsequently\, we focus on the ca
se of two clusters and derive sufficient conditions under which the propos
ed LP relaxation recovers the underlying clusters exactly. Namely\, we con
sider the stochastic ball model\, a popular generative model for K-means c
lustering\, and we show that if the separation distance between cluster ce
nters satisfies $\\Delta > 1+\\sqrt 3$\, then the LP relaxation recovers t
he planted clusters with high probability. This is a major improvement ove
r the only existing recovery guarantee for an LP relaxation of K-means clu
stering stating that recovery is possible with high probability if and onl
y if $\\Delta > 4$. Our numerical experiments indicate that the proposed L
P relaxation significantly outperforms a popular semidefinite programming
relaxation in recovering the planted clusters.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jean Pauphilet (London Business School)
DTSTART:20200630T183000Z
DTEND:20200630T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/20
DESCRIPTION:Title: A Unified Approach to Mixed-Integer Optimization: Nonlinear Formulati
ons and Scalable Algorithms\nby Jean Pauphilet (London Business School
) as part of Discrete Optimization Talks\n\n\nAbstract\nWe propose a unifi
ed framework to address a family of classical mixed-integer optimization p
roblems with semicontinuous decision variables\, including network design\
, facility location\, unit commitment\, sparse portfolio selection\, binar
y quadratic optimization\, sparse principal component analysis\, and spars
e learning problems. These problems exhibit logical relationships between
continuous and discrete variables\, which are usually reformulated linearl
y using a big-M formulation. In this work\, we challenge this longstanding
modeling practice and express the logical constraints in a non-linear way
. By imposing a regularization condition\, we reformulate these problems a
s convex binary optimization problems\, which are solvable using an outer-
approximation procedure. Numerically\, we establish that a general-purpose
strategy\, combining cutting-plane\, first-order\, and local search metho
ds\, solves these problems faster and at a larger scale than MICO solvers.
For instance\, our approach successfully solves network design problems w
ith 100s of nodes and provides solutions up to 40% better.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jannis Kurtz (University of Siegen)
DTSTART:20200714T180000Z
DTEND:20200714T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/21
DESCRIPTION:Title: Discrete Optimization Methods for Group Model Selection in Compressed
Sensing\nby Jannis Kurtz (University of Siegen) as part of Discrete O
ptimization Talks\n\n\nAbstract\nWe study the problem of signal recovery f
or group models. More precisely for a given set of groups\, each containin
g a small subset of indices\, and for given linear sketches of the true si
gnal vector which is known to be group-sparse in the sense that its suppor
t is contained in the union of a small number of these groups\, we study a
lgorithms which successfully recover the true signal just by the knowledge
of its linear sketches. We consider two versions of the classical Iterati
ve Hard Thresholding algorithm (IHT). The classical version iteratively ca
lculates the exact projection of a vector onto the group model\, while the
approximate version (AM-IHT) uses a head- and a tail-approximation iterat
ively. We apply both variants to group models and analyse the two cases wh
ere the sensing matrix is a Gaussian matrix and a model expander matrix.\n
\nTo solve the exact projection problem on the group model\, which is know
n to be equivalent to the maximum weight coverage problem\, we use discret
e optimization methods based on dynamic programming and Benders' Decomposi
tion. The head- and tail-approximations are derived by a classical greedy-
method and LP-rounding\, respectively.\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Merve Bodur (University of Toronto)
DTSTART:20210129T180000Z
DTEND:20210129T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/22
DESCRIPTION:Title: Inverse Mixed Integer Optimization: Polyhedral Insights and Trust Reg
ion Methods\nby Merve Bodur (University of Toronto) as part of Discret
e Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ward Romeijnder (University of Groningen)
DTSTART:20210129T183000Z
DTEND:20210129T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/23
DESCRIPTION:Title: A Converging Benders’ Decomposition Algorithm for Two-Stage Mixed-I
nteger Recourse Models\nby Ward Romeijnder (University of Groningen) a
s part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thibaut Vidal (Polytechnique Montréal)
DTSTART:20210226T180000Z
DTEND:20210226T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/24
DESCRIPTION:Title: Born-Again Tree Ensembles: Seeing the Forest for the Trees\nby T
hibaut Vidal (Polytechnique Montréal) as part of Discrete Optimization Ta
lks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marianna De Santis (Sapienza Università di Roma)
DTSTART:20210226T183000Z
DTEND:20210226T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/25
DESCRIPTION:Title: Exact approaches for multiobjective mixed integer nonlinear programmi
ng problems\nby Marianna De Santis (Sapienza Università di Roma) as p
art of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yiling Zhang (University of Minnesota)
DTSTART:20210326T173000Z
DTEND:20210326T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/26
DESCRIPTION:Title: Building Load Control using Distributionally Robust Binary Chance-Con
strained Programs with Right-Hand Side Uncertainty and the Adjustable Vari
ants\nby Yiling Zhang (University of Minnesota) as part of Discrete Op
timization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Robert Hildenbrand (Virginia Tech)
DTSTART:20210430T170000Z
DTEND:20210430T173000Z
DTSTAMP:20260404T111102Z
UID:DOTs/27
DESCRIPTION:Title: Compact mixed-integer programming relaxations in quadratic optimizati
on\nby Robert Hildenbrand (Virginia Tech) as part of Discrete Optimiza
tion Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ruth Misener (Imperial College)
DTSTART:20210430T173000Z
DTEND:20210430T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/28
DESCRIPTION:Title: Partial Lasserre Relaxation for Sparse Max Cut\nby Ruth Misener (
Imperial College) as part of Discrete Optimization Talks\n\nAbstract: TBA\
n
LOCATION:https://stable.researchseminars.org/talk/DOTs/28/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Andrew Trapp (Worcester Polytechnic Institute)
DTSTART:20210326T173000Z
DTEND:20210326T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/29
DESCRIPTION:Title: A Comparative Study of Stability Representations for Solving Many-to-
One Matching Problems with Ties and Incomplete Lists via Integer Optimizat
ion\nby Andrew Trapp (Worcester Polytechnic Institute) as part of Disc
rete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yuri Faenza (Columbia University)
DTSTART:20211015T170000Z
DTEND:20211015T173000Z
DTSTAMP:20260404T111102Z
UID:DOTs/30
DESCRIPTION:Title: Stable matchings\, lattices\, and polytopes\nby Yuri Faenza (Colu
mbia University) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/30/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Margarida Carvalho (Polytechnique Montréal)
DTSTART:20211015T173000Z
DTEND:20211015T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/31
DESCRIPTION:Title: Sequential matching markets\nby Margarida Carvalho (Polytechnique
Montréal) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marc Pfetsch (TU Darmstadt)
DTSTART:20220128T180000Z
DTEND:20220128T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/32
DESCRIPTION:Title: Presolving for Mixed-Integer Semidefinite Optimization\nby Marc P
fetsch (TU Darmstadt) as part of Discrete Optimization Talks\n\nAbstract:
TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Victor Zavala (University of Wisconsin-Madison)
DTSTART:20220128T183000Z
DTEND:20220128T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/33
DESCRIPTION:Title: Solution of Large-Scale Supply Chain Models using Graph Sampling & Co
arsening\nby Victor Zavala (University of Wisconsin-Madison) as part o
f Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Carla Michini (University of Wisconsin-Madison)
DTSTART:20211119T180000Z
DTEND:20211119T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/34
DESCRIPTION:Title: The Price of Anarchy in Series-Parallel Network Congestion Games\
nby Carla Michini (University of Wisconsin-Madison) as part of Discrete Op
timization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/34/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Christian Tjandraatmadja (Google)
DTSTART:20211119T183000Z
DTEND:20211119T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/35
DESCRIPTION:Title: Constrained Discrete Black-Box Optimization using Mixed-Integer Progr
amming\nby Christian Tjandraatmadja (Google) as part of Discrete Optim
ization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/35/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ivana Ljubic (ESSEC Business School of Paris)
DTSTART:20211210T180000Z
DTEND:20211210T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/36
DESCRIPTION:Title: Lower Bounds for Ramsey Numbers on Circulant Graphs: A Bilevel Optimi
zation Approach\nby Ivana Ljubic (ESSEC Business School of Paris) as p
art of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Danial Davarnia (Iowa State University)
DTSTART:20220225T180000Z
DTEND:20220225T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/37
DESCRIPTION:Title: Rectangular decomposition of mixed integer programs via decision diag
rams with application to unit commitment\nby Danial Davarnia (Iowa Sta
te University) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/37/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Michael Poss (LIRMM)
DTSTART:20220225T183000Z
DTEND:20220225T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/38
DESCRIPTION:Title: Optimization problems in graphs with locational uncertainty\nby M
ichael Poss (LIRMM) as part of Discrete Optimization Talks\n\nAbstract: TB
A\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/38/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jorge Sefair (Arizona State University)
DTSTART:20220325T170000Z
DTEND:20220325T173000Z
DTSTAMP:20260404T111102Z
UID:DOTs/39
DESCRIPTION:Title: Continuous location models in the presence of obstacles: an applicati
on to wireless sensor networks\nby Jorge Sefair (Arizona State Univers
ity) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/39/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alexandra Newman (Colorado School of Mines)
DTSTART:20220325T173000Z
DTEND:20220325T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/40
DESCRIPTION:Title: Optimizing Design and Operations of Concentrated Solar Power Plants\nby Alexandra Newman (Colorado School of Mines) as part of Discrete Opt
imization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/40/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Swati Gupta (Georgia Tech)
DTSTART:20220429T170000Z
DTEND:20220429T173000Z
DTSTAMP:20260404T111102Z
UID:DOTs/41
DESCRIPTION:Title: Reusing Combinatorial Structure for Projections over Submodular Polyt
opes\nby Swati Gupta (Georgia Tech) as part of Discrete Optimization T
alks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/41/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Emma Frejinger (Université de Montréal)
DTSTART:20220429T173000Z
DTEND:20220429T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/42
DESCRIPTION:Title: Fast Heuristic L-Shaped Method Through Machine Learning\nby Emma
Frejinger (Université de Montréal) as part of Discrete Optimization Talk
s\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/42/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Laura Sanità (Bocconi University)
DTSTART:20220923T170000Z
DTEND:20220923T173000Z
DTSTAMP:20260404T111102Z
UID:DOTs/43
DESCRIPTION:Title: On the Simplex method for 0/1 polytopes\nby Laura Sanità (Boccon
i University) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/43/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Elina Rönnberg
DTSTART:20220923T173000Z
DTEND:20220923T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/44
DESCRIPTION:Title: Integer programming column generation: Accelerating branch-and-price
for set covering\, packing\, and partitioning problems\nby Elina Rönn
berg as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/44/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anirudh Subramanyam (Penn State)
DTSTART:20221028T170000Z
DTEND:20221028T173000Z
DTSTAMP:20260404T111102Z
UID:DOTs/45
DESCRIPTION:Title: Branch-price-and-cut algorithms for robust vehicle routing under unce
rtainty\nby Anirudh Subramanyam (Penn State) as part of Discrete Optim
ization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/45/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sophie Huiberts (Columbia University)
DTSTART:20221028T173000Z
DTEND:20221028T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/46
DESCRIPTION:Title: Integer programming column generation: Accelerating branch-and-price
for set covering\, packing\, and partitioning problems\nby Sophie Huib
erts (Columbia University) as part of Discrete Optimization Talks\n\nAbstr
act: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/46/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Joseph Paat (University of British Columbia)
DTSTART:20221118T180000Z
DTEND:20221118T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/47
DESCRIPTION:Title: The column-number of a Delta-modular matrix\nby Joseph Paat (Univ
ersity of British Columbia) as part of Discrete Optimization Talks\n\nAbst
ract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/47/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Christopher Hojny (Eindhoven University of Technology)
DTSTART:20221118T183000Z
DTEND:20221118T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/48
DESCRIPTION:Title: The role of rationality in integer programming relaxations\nby Ch
ristopher Hojny (Eindhoven University of Technology) as part of Discrete O
ptimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/48/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Claudio Contardo
DTSTART:20221209T180000Z
DTEND:20221209T183000Z
DTSTAMP:20260404T111102Z
UID:DOTs/49
DESCRIPTION:Title: MIP-based branch-and-bound for the discrete ordered median problem\nby Claudio Contardo as part of Discrete Optimization Talks\n\nAbstract:
TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/49/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anand Subramanian (Universidade Federal da Paraíba)
DTSTART:20221209T183000Z
DTEND:20221209T190000Z
DTSTAMP:20260404T111102Z
UID:DOTs/50
DESCRIPTION:Title: Conference scheduling: a clustering-based approach\nby Anand Subr
amanian (Universidade Federal da Paraíba) as part of Discrete Optimizatio
n Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/50/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Harsha Nagarajan (Los Alamos National Laboratory)
DTSTART:20230224T170000Z
DTEND:20230224T173000Z
DTSTAMP:20260404T111102Z
UID:DOTs/51
DESCRIPTION:Title: Learning to Accelerate Partitioning-based Algorithms for the Global O
ptimization of Non-convex Programs\nby Harsha Nagarajan (Los Alamos Na
tional Laboratory) as part of Discrete Optimization Talks\n\nAbstract: TBA
\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/51/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kevin Tierney (Universität Bielefeld)
DTSTART:20230224T173000Z
DTEND:20230224T180000Z
DTSTAMP:20260404T111102Z
UID:DOTs/52
DESCRIPTION:Title: Search heuristics for solving combinatorial optimization problems wit
h deep reinforcement learning\nby Kevin Tierney (Universität Bielefel
d) as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/52/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marcia Fampa (Universidade Federal do Rio de Janeiro)
DTSTART:20230324T160000Z
DTEND:20230324T163000Z
DTSTAMP:20260404T111102Z
UID:DOTs/53
DESCRIPTION:by Marcia Fampa (Universidade Federal do Rio de Janeiro) as pa
rt of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/53/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Emily Tucker (Clemson University)
DTSTART:20230324T163000Z
DTEND:20230324T170000Z
DTSTAMP:20260404T111102Z
UID:DOTs/54
DESCRIPTION:by Emily Tucker (Clemson University) as part of Discrete Optim
ization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/54/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yatharth Dubey (University of Illinois at Urbana-Champaign)
DTSTART:20230428T160000Z
DTEND:20230428T163000Z
DTSTAMP:20260404T111102Z
UID:DOTs/55
DESCRIPTION:by Yatharth Dubey (University of Illinois at Urbana-Champaign)
as part of Discrete Optimization Talks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/55/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Georgina Hall (INSEAD)
DTSTART:20230428T163000Z
DTEND:20230428T170000Z
DTSTAMP:20260404T111102Z
UID:DOTs/56
DESCRIPTION:by Georgina Hall (INSEAD) as part of Discrete Optimization Tal
ks\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/DOTs/56/
END:VEVENT
END:VCALENDAR