Conferences  >  Mathematics  >  Optimization Theory, Operational Research  >  United States

Institute for Pure and Applied Mathematics (IPAM), UCLA

In recent years, the use of Machine Learning techniques to Operations Research (OR) problems, especially in the Discrete Optimization (DO) a.k.a. Combinatorial Optimization context, opens very interesting scenarios because DO is the “home” of an endless list of decision-making problems that are of fundamental importance in multitude applications. The workshop will bring together experts in mathematics (optimization, graph theory, sparsity, combinatorics, statistics), operations research (assignment problems, routing, planning, Bayesian search, automation, scheduling), machine learning (deep learning, supervised, self-supervised and reinforcement learning) and artificial intelligence at large (including multi-agent systems, interpretability, fairness, etc.).

Neural Networks and Artificial Intelligence, Machine Learning,    Graph Theory and Combinatorics

ICERM / Brown University

This workshop focuses on the latest advances in both areas, Mixed-Integer Linear and Non-Linear Optimization. The workshop is a forum for presenting the latest advances as well as serving as a crucible for new research in these areas.

ICERM / Brown University

Combinatorial optimization is an active research field in mathematics, with an immense range of applications. This workshop will bring together researchers and leading experts interested in the mathematical foundations of combinatorial optimization algorithms to discuss new tools and methods, in particular regarding the use of algebraic, analytical, and geometric techniques. Special emphasis will be given on polyhedral methods, since they are at the core of several groundbreaking combinatorial optimization results developed in recent years.

ICERM / Brown University

The aim of this workshop is to discuss many exciting recent developments on the computational side of discrete optimization. The workshop has three main themes. The first theme is that of commercial and academic/open-source solvers that have allowed the solution of very large-scale problems, and of recent developments in exact solvers that have allowed for proofs of results in logic, knot theory, and combinatorics. The second theme is the interaction between optimization and machine learning: these two areas complement each other in several ways. The third theme is quantum computing and unconventional computing architectures: quantum computing has been used to tackle combinatorial optimization problems, and quantum algorithms exist for other related optimization problems such as linear and semidefinite relaxations.

Society for Industrial and Applied Mathematics (SIAM)

University of Wisconsin–Madison

The IPCO conference is under the auspices of the Mathematical Optimization Society and is held every year. The conference is a forum for researchers and practitioners working on various aspects of integer programming and combinatorial optimization. The aim is to present recent developments in theory, computation, and applications in these areas.

Integer Programming, Combinatorial Optimization

The organizers of the XVI International Conference on Stochastic Programming (ICSP2022) are honored to host the tri-annual Conference at the University of California, Davis Graduate School of Management. The ICSP is the premier event of the Stochastic Programming Society (SPS) , a technical session of the Mathematical Optimization Society that brings together researchers who work on decisions under uncertainty, practitioners in the industrial and institutional sectors share recent theoretical and applied results. The conference aims to present the state-of-the-art in this field as well as neighboring scientific areas.