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Stream 1a: Nonlinear Programming I
MB39 — Advances in numerical methods for large scale nonlinear optimization and applications I
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Exploring the Spectral Proximal Gradient Method and Applications
Paulo J. S. Silva, Universidade Estadual De Campinas -
Regularized methods via cubic model subspace minimization for nonconvex optimization
Margherita Porcelli, Università Di Firenze -
Scalable interior point methods for large scale discrete optimal transport problems
Filippo Zanetti, University of Edinburgh -
Recent advances in preconditioners for interior point methods
Jacek Gondzio, University of Edinburgh
MB62 — Development of nonlinear optimization algorithms and applications
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Improved lower bounds for stochastic optimization under Markovian sampling
Ermin Wei, Northwestern University -
Designing surrogate functions for reinforcement learning
Nicolas Le Roux, Microsoft Research -
The Exactness of the L1 Penalty Function for a Class of Mathematical Programs with Generalized Complementarity Constraints
Xin Liu, Academy Of Mathematics And Systems Science, Chinese Academy Of Sciences -
Low-rank optimization on Tucker tensor varieties
Bin Gao, Academy Of Mathematics And Systems Science
MB11 — Advances in Large-Scale Nonlinear Optimization I
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Robustly Stable Accelerated Gradient Methods
Mert Gurbuzbalaban, Rutgers University -
Advanced, Adaptive and Flexible Algorithms for Decentralized Optimization
Albert Berahas, University of Michigan -
Stochastic Interior-Point method for Inequality constrained optimization
Qi Wang, Lehigh University -
** CANCELLED ** Nonsmooth Optimization on a Finer Scale: Bounded Local Subgradient Variation Perspective
Jelena Diakonikolas, University of Wisconsin Madison
Stream 1c: Nonsmooth Optimization
MB71 — Inertial and stochastic algorithms
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Convergence of Momentum Stochastic Gradient Descent
Sebastian Kassing, University of Bielefeld -
Convergence of a Normal Map-Based Prox-SGD Method for Stochastic Composite Optimization
Junwen Qiu, The Chinese University of Hong Kong, Shenzhen -
Stochastic composition optimization in the absence of Lipschitz continuous gradient
Sam Davanloo Tajbakhsh, The Ohio State University -
Adam-family Methods with Decoupled Weight Decay in Deep Learning
Kuangyu Ding, National University of Singapore
MB92 — Douglas--Rachford Awesome Cones, Great Job! 1
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On tight error bounds for conic optimisation
Scott Boivin Lindstrom, Curtin University -
On particularly awesome cones
Vera Roshchina, UNSW Sydney -
Proximal splitting methods avoid strict saddle points of weakly convex problems
Felipe Atenas, The University of Melbourne -
Solving large-scale basis pursuit by infeasible alternating projections
Luiz Rafael Santos, Universidade Federal De Santa Catarina
Stream 1d: Semi-Definite Programming
MB6 — Convex Approaches for Quadratically Constrained Quadratic Programs
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Reformulation-Perspectification Technique for Global Optimization
Jianzhe Zhen, Swissquant -
A Slightly Lifted Convex Relaxation for Nonconvex Quadratic Programming with Ball Constraints
Samuel Burer, University of Iowa -
** Moved to Friday at 14:00 - FB821** Semidefinite representable reformulations for two variants of the trust-region subproblem
Boshi Yang, Clemson University -
Hidden convexity, optimization and algorithms on rotation matrices
Akshay Ramachandran, CWI And University of Amsterdam
MB29 — Recent advances in conic optimization - Part 1
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** Permuted with Garner's talk in FA17 ** Procrustean Data Collaboration for Cross-Silo Privacy-Preserving Machine Learning
Keiyu Nosaka, Graduate School Of Science And Technology University of Tsukuba -
Riemannian Interior Point Methods for Constrained Optimization on Manifolds
Akiko Yoshise, University of Tsukuba -
Projection Onto Convex Sets Approach in Solving Homogeneous Self-Dual Model of Quadratically Linear Programming Problems (Case Study: Portfolio Optimization)
Sena Safarina, Institut Teknologi Sepuluh Nopember -
Dual Spectral Projected Gradient Method for Generalized Log-det Semidefinite Programming
Charles Namchaisiri, Tokyo Institute of Technology
Stream 1e: Variational Analysis, Variational Inequalities and Complementarity
MB312 — Convex analysis and beyond
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Polar convexity in finite dimensional Euclidean spaces
Shubhankar Bhatt, University of Western Ontario -
The uniqueness of Lyapunov rank among symmetric cones
Michael Orlitzky, UMBC -
Constructions of c-splitting potentials for multi-marginal monotone sets
Sedi Bartz, University of Massachusetts Lowell -
Exploring averages of finite sets in nonpositively curved metric spaces
Adrian Lewis, Cornell University
Stream 1g: Derivative-free and Simulation-based Optimization
MB302 — Challenging DFO and SBO Applications 1
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Optimization under hidden constraints and its application to the robust design of wind turbines
Morgane Menz, IFP Energies Nouvelles -
Black-box optimization for the design of a jet plate for impingement cooling
Filippo Marini, University of Bologna -
Blackbox optimization for origami-inspired multistable structures
Luca Boisneault, Polytechnique Montréal -
Convex optimization of vertical alignment of roads
Yves Lucet, UBC
Stream 1h: Optimal Control, PDE Constrained Optimization, and Multi-level Methods
MB88 — Nonsmooth PDE Constrained Optimization 1
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PDE Constrained Optimization and Digital Twins
Harbir Antil, George Mason University -
Semi-smoothness in Infinite Dimensional Non-Smooth Optimization
Anton Schiela, University of Bayreuth -
A numerical solution approach for non-smooth optimal control problems based on the Pontryagin maximum principle
Daniel Wachsmuth, University of Wuerzburg -
Interweaved first-order methods for PDE-constrained and bilevel optimisation
Tuomo Valkonen, Escuela Politécnica Nacional
Stream 2a: Mixed Integer Linear Programming
MB157 — Vehicle Routing Problems under Uncertainty
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Approximating the recourse cost function of the vehicle routing problem with stochastic demands.
Matheus Jun Ota, University of Waterloo -
Distributionally Robust Cyclic Inventory Routing
Albert Schrotenboer, Eindhoven University of Technology -
Polynomial-time separation algorithms based on closed-form equations for the RVRP under travel time uncertainty
Rafael Ajudarte De Campos, Université Laval -
Pickup and Delivery Vehicle Routing Problems under Uncertainty via Robust Optimization
Pedro Munari, Federal University of São Carlos
Stream 2b: Mixed Integer Nonlinear Programming
MB12 — Binary Quadratic Programming
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Matrix generation for binary quadratic programming
Lucas Létocart, LIPN, University Sorbonne Paris Nord, CNRS -
Exact and Approximate Solution Methods for Convex Binary Quadratic Optimization
Borzou Rostami, University of Alberta -
Binary Quadratic Optimization: A Polyhedral Characterization of Linearizable Instances
Lucas Waddell, Bucknell University -
Inductive Linearization for Binary Quadratic Programs
Sven Mallach, University of Bonn
Stream 2c: Combinatorial Optimization and Graph Theory
MB223 — Complexity and algorithmic aspects of structured families of graphs 1
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Local Certification of Geometric Graph Classes
Aurélie Lagoutte, University Grenoble Alpes, France -
Topological tools for H-recoloring and H-mixing
Moritz Mühlenthaler, G Scop, Grenoble Inp, Université Grenoble Alpes -
Hereditary properties defined by 2-edge-colourings avoiding a set of 2-edge-coloured graphs
César Hernández Cruz, Universidad Nacional Autónoma De México -
Splitting-off in Hypergraphs
Karthekeyan Chandrasekaran, University of Illinois, Urbana Champaign
MB921 — Combinatorial Optimization and Graph Theory 1
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Weighted graph burning: Complexity and MIP formulations
Stephan Marnach, Rwth Aachen University -
Computing cuts in graphs: Homory-Hu tree and the Sparsest Cut problem
Vladimir Kolmogorov, Institute of Science And Technology Austria (Ista) -
On the Polynomial Solvability of the Two-Stage Robust Perfect b-Matching Problem
Jenny Segschneider, Rwth Aachen -
Spectral sparsification of hypergraphs
Tasuku Soma, Institute of Statistical Mathematics
Stream 2d: Machine Learning and Discrete Optimization
MB248 — Distance Optimization in Data Science
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A Novel Column Generation Framework for One-for-many Counterfactual Explanations
Jasone Ramirez Ayerbe, Universidad De Sevilla -
Solving the Contextual Multiobjective Inverse Ideal Point Problem via Mathematical Optimization
Nuria Gómez Vargas, University of Seville -
Distance-Based Fairness in Classification and Regression
Thomas Halskov, Copenhagen Business School -
Sequential Counterfactual Decisions
Emilio Carrizosa, Universidad De Sevilla
Stream 2e: Approximation and Online Algorithms
MB153 — Stochastic Combinatorial Optimization I
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Stochastic Scheduling of Bernoulli Jobs
Marc Uetz, University of Twente -
Approximation Algorithms for Stochastic Minimum Norm Combinatorial Optimization
Sharat Ibrahimpur, London School Of Economics -
Learning Prophet Inequality and Pandora's Box with Limited Feedback
Thomas Kesselheim, University of Bonn -
Set Selection under Explorable Stochastic Uncertainty
Nicole Megow, University of Bremen
MB239 — Network design
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Single-source unsplittable flows in planar graphs
Laura Vargas Koch, University of Bonn -
New Structures and Algorithms for Length-Constrained Expander Decompositions
Ellis Hershkowitz, Brown University -
Ghost Value Augmentation for k-Edge-Connectivity
Nathan Klein, Institute of Advanced Study -
LP-Based Algorithms for Two-Cost Network Design
Rhea Jain, University of Illinois At Urbana Champaign
Stream 2f: Computational Discrete and Integer Optimization
MB344 — Modeling Software
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FICO® Xpress: New and Augmented Interfaces for Modeling Optimization Problems
Gregor Hendel, Fair Isaac Deutschland Gmbh -
Practical guidelines for model improvement and reformulation
Dan Steffy, Gurobi Optimization -
What's new in JuMP
Miles Lubin, Hudson River Trading -
OptiChat: An AI Assistant for Explaining Optimization Models Powered by LLM
Can Li, Purdue University
MB932 — Computational Discrete and Integer Optimization 1
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Optimizing Airport Selection for Electric Aircraft Operations Across Europe
Frederic Kroner, Tu Braunschweig -
A speed-up for Helsgaun's TSP heuristic by relaxing the positive gain criterion
Sabrina Ammann, Tu Braunschweig -
A New Labeling Approach for a Special Case of the Multicommodity Flow Problem
Lisa Marie Manke, Tu Braunschweig, Institute of Automotive Management And Industrial Production -
Hexaly, a new kind of global optimization solver
Fred Gardi, Hexaly
Stream 3a: Continuous Stochastic Programming
MB107 — Multistage Stochastic Programs
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A Bayesian approach to data-driven multi-stage stochastic optimization
Zhiping Chen, Xi’an Jiaotong University -
Multistage stochastic optimization of an elementary hydrogen infrastructure
Raian Noufel Lefgoum, école Des Ponts Paristech Cermics -
The Kelly Strategy - Modifications to Address Market Changes and Investor Preferences
Leonard Maclean, Dalhousie University -
Multi-stage distributionally robust optimization with Bayesian learning moments uncertainty
Jia Liu, Xi'an Jiaotong University
Stream 3b: Discrete Stochastic Programming
MB287 — Recent modeling language extensions: Stochastics and beyond
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GAMSPy: Algebraic Modeling and Python
Steven Dirkse, GAMS Development Corporation -
DAG-based modeling framework for extended mathematical programming
Olivier Huber, University of Wisconsin Madison -
Advances in Automated Conversion of Optimization Problems
Robert Fourer, AMPL Optimization Inc. -
Stochastic Equilibrium Problems
Michael Ferris, University of Wisconsin Madison
Stream 3c: Robust and Distributionally Robust Optimization
MB108 — Applications of Robust and Distributionally Robust Optimization
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Budget-Driven Multi-Period Hub Location: A Robust Time Series Approach
Zhi Chen, The Chinese University of Hong Kong -
Network Flow Models for Robust Binary Optimization with Selective Adaptability
Ian Yihang Zhu, NUS Business School -
Robust Optimization with Moment-Dispersion Ambiguity
Li Chen, The University of Sydney -
Wasserstein Regularization for 0-1 Loss
Zhen Yang, The University of Texas at Austin
Stream 3d: Multi-stage Stochastic Programming and Reinforcement learning
MB167 — Reinforcement Learning in Industry
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Adaptive Experimentation at Scale: A Computational Framework
Ethan Che, Columbia Business School -
Exploration Incentives in Model-Based Reinforcement Learning
Alec Koppel, Ai Research, Jp Morgan Chase -
RL and ADP for Rideshare Operations
Zhiwei (Tony) Qin, LYFT -
Deep Reinforcement Learning for Recommender Systems and Beyond
Zheqing Zhu, Meta AI
Stream 3e: Data-driven optimization
MB254 — Data-driven optimization in urban systems
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Network Flow Problems with Electric Vehicles
Haripriya Pulyassary, Cornell University -
Pricing shared rides
Julia Yan, University of British Columbia -
A Generative Learning Approach for Data-Driven Robust Optimization
Aron Brenner, Massachusetts Institute of Technology -
Information design for spatial resource allocation
Manxi Wu, Cornell University
Stream 4b: Transportation and logistics
MB190 — Network Design for Transportation Planning II
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The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem
Julie Kienzle, University of Montreal -
Multi-layer network design
Teodor Gabriel Crainic, CIRRELT And Université du Québec à Montréal -
The Integrated Multi-Tier Hierarchical Hub Location and Scheduled Service Network Design Problem
Amirmohammad Fathollahifard, University of Quebec In Montreal -
Profit maximizing network design problem with location decisions considering incompatible commodities and binary demand selection
Ehsan Mirzaei, Université De Montréal Et Tarbiat Modares University (Iran)
Stream 4d: Energy and Environment
MB119 — Optimization models for electric vehicle charging problems 1
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Strengths of the Formulations of the Fuel Refuelling Location Problem
Nagisa Sugishita, Université De Montréal -
A Constrained Traffic Assignment Problem Model for Congestion Analysis of Electric Vehicle Fast Chargers
Ismail Sevim, Universite De Montreal -
Optimal Electric Vehicle Charging with Dynamic Pricing, Customer Preferences and Power Peak Reduction
Gaël Guillot, INRIA -
Optimizing Electric Vehicle Charger Locations for Ride-hailing Services through Discrete Simulation-based Optimization
Tommaso Schettini, HEC Montréal
Stream 4e: Healthcare and life sciences
MB219 — Applications of Stochastic optimization in Healthcare
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A Network-Flow Approach to Increasing Inter-Facility Transfers for Nova Scotia Health Long-Term Care
Amelia Lane, Dalhousie University -
Quantifying the benefits of customized vaccination strategies: A network-based optimization approach
Su Li, Texas A&M University -
Multi-objective scheduling of chemotherapy drug preparation
Camille Pinçon, Polytechnique Montréal -
Real-time demand-driven inventory management in a hospital pharmacy
Ali Jafari, Polytechnique Montreal
Stream 4g: High performance implementation and quantum computing
MB209 — A glimpse of computational optimization at Google I
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Practical Performance Guarantees for Pipelined DNN Inference
Matthew Fahrbach, Google Research -
Contextual Nearest Neighbor Search in Sublinear Time
Mohammad Hajiaghayi, University of Maryland -
Multi-Vector Retrieval: The New Frontier of Similarity Search
Rajesh Jayaram, Google Research Nyc -
Attention-based Model Structure Optimization
Hossein Bateni, Google Research
MB162 — Quantum Computing and Optimization I
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Symplectic Extra-gradient Type Method for Solving General Non-monotone Inclusion Problem
Yi Zhang, Institute of Computational Mathematics And Scientific/Engineering Computing -
Quantum Algorithms for Multi-objective Optimization
Stefan Woerner, IBM Quantum -
Solving the semidefinite relaxation of QUBOs in matrix multiplication time, and faster with a quantum computer
Giacomo Nannicini, University of Southern California -
Quantum Langevin Dynamics for Optimization
Yuchen Lu, Tsinghua University
Stream 4f: Finance and social sciences
MB960 — Finance and social sciences 1
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Optimal hedging of the interest rate swap book
Jörgen Blomvall, Linköping University -
Predicting Limit Order Book Prices: A Machine Learning Approach
Francis Huot Chantal, Université De Montréal -
Contextual Portfolio Optimization: Some Recent Results
Roy Kwon, University of Toronto -
Fair Mixed Effects Support Vector Machine
João Vitor Pamplona, Trier University
Stream 4h: Game Theory
MB962 — Game Theory 1
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A new approach for solving linear programming problems with dense columns
Aurelio Oliveira, Unicamp -
A New Strategy for Solving Cournot Games
Nena Batenburg, University of Copenhagen -
Mitigation mechanisms for externalities in intertemporal multistage markets: The case of cascaded hydropower plants
Gabriel Cunha, PSR -
Counterparty credit risk under netting agreements: A dynamic game Interpretation
Ahmadreza Tavasoli, HEC Montréal