Combinatorial optimization | Theoretical computer science | Computational complexity theory
Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set. Typical combinatorial optimization problems are the travelling salesman problem ("TSP"), the minimum spanning tree problem ("MST"), and the knapsack problem. In many such problems, such as the ones previously mentioned, exhaustive search is not tractable, and so specialized algorithms that quickly rule out large parts of the search space or approximation algorithms must be resorted to instead. Combinatorial optimization is related to operations research, algorithm theory, and computational complexity theory. It has important applications in several fields, including artificial intelligence, machine learning, auction theory, software engineering, applied mathematics and theoretical computer science. Some research literature considers discrete optimization to consist of integer programming together with combinatorial optimization (which in turn is composed of optimization problems dealing with graph structures), although all of these topics have closely intertwined research literature. It often involves determining the way to efficiently allocate resources used to find solutions to mathematical problems. (Wikipedia).
Daniel Dadush: Friendly smoothed analysis of the simplex method
The lecture was held within the framework of the follow-up workshop to the Hausdorff Trimester Program: Combinatorial Optimization.
From playlist Follow-Up-Workshop "Combinatorial Optimization"
Nikhil Bansal: On a generalization of iterated and randomized rounding
The lecture was held within the framework of the follow-up workshop to the Hausdorff Trimester Program: Combinatorial Optimization. We describe a new rounding procedure that optimally combines the benefits of both iterated rounding and randomized rounding. A nice feature of this procedure
From playlist Follow-Up-Workshop "Combinatorial Optimization"
Algebraic and Convex Geometry of Sums of Squares on Varieties (Lecture 1) by Greg Blekherman
PROGRAM COMBINATORIAL ALGEBRAIC GEOMETRY: TROPICAL AND REAL (HYBRID) ORGANIZERS: Arvind Ayyer (IISc, India), Madhusudan Manjunath (IITB, India) and Pranav Pandit (ICTS-TIFR, India) DATE & TIME: 27 June 2022 to 08 July 2022 VENUE: Madhava Lecture Hall and Online Algebraic geometry is t
From playlist Combinatorial Algebraic Geometry: Tropical and Real (HYBRID)
Optimization and Tropical Combinatorics (Lecture 1) by Michael Joswig
PROGRAM COMBINATORIAL ALGEBRAIC GEOMETRY: TROPICAL AND REAL (HYBRID) ORGANIZERS: Arvind Ayyer (IISc, India), Madhusudan Manjunath (IITB, India) and Pranav Pandit (ICTS-TIFR, India) DATE & TIME: 27 June 2022 to 08 July 2022 VENUE: Madhava Lecture Hall and Online Algebraic geometry is t
From playlist Combinatorial Algebraic Geometry: Tropical and Real (HYBRID)
Circular Fence Posets and Associated Polytopes with Unexpected Symmetry by Mohan Ravichandran
PROGRAM COMBINATORIAL ALGEBRAIC GEOMETRY: TROPICAL AND REAL (HYBRID) ORGANIZERS: Arvind Ayyer (IISc, India), Madhusudan Manjunath (IITB, India) and Pranav Pandit (ICTS-TIFR, India) DATE & TIME: 27 June 2022 to 08 July 2022 VENUE: Madhava Lecture Hall and Online Algebraic geometry is t
From playlist Combinatorial Algebraic Geometry: Tropical and Real (HYBRID)
Bistra Dilkina: "Decision-focused learning: integrating downstream combinatorics in ML"
Deep Learning and Combinatorial Optimization 2021 "Decision-focused learning: integrating downstream combinatorics in ML" Bistra Dilkina - University of Southern California (USC) Abstract: Closely integrating ML and discrete optimization provides key advantages in improving our ability t
From playlist Deep Learning and Combinatorial Optimization 2021
optimization and Tropical Combinatorics (Lecture 3) by Michael Joswig
PROGRAM COMBINATORIAL ALGEBRAIC GEOMETRY: TROPICAL AND REAL (HYBRID) ORGANIZERS Arvind Ayyer (IISc, India), Madhusudan Manjunath (IITB, India) and Pranav Pandit (ICTS-TIFR, India) DATE: 27 June 2022 to 08 July 2022 VENUE: Madhava Lecture Hall and Online Algebraic geometry is the study of
From playlist Combinatorial Algebraic Geometry: Tropical and Real (HYBRID)
Zico Kolter: "Fast semidefinite programming for (differentiable) combinatorial optimization"
Deep Learning and Combinatorial Optimization 2021 "Fast semidefinite programming for (differentiable) combinatorial optimization" Zico Kolter - Carnegie Mellon University Institute for Pure and Applied Mathematics, UCLA February 25, 2021 For more information: https://www.ipam.ucla.edu/d
From playlist Deep Learning and Combinatorial Optimization 2021
Aaron Sidford: Introduction to interior point methods for discrete optimization, lecture I
Over the past decade interior point methods (IPMs) have played a pivotal role in mul- tiple algorithmic advances. IPMs have been leveraged to obtain improved running times for solving a growing list of both continuous and combinatorial optimization problems including maximum flow, bipartit
From playlist Summer School on modern directions in discrete optimization
Wouter Kool: Deep Learning for Combinatorial Optimization: count your flops & make your flops count!
Deep Learning and Combinatorial Optimization 2021 "Deep Learning for Combinatorial Optimization: count your flops and make your flops count!" Wouter Kool - University of Amsterdam Abstract: Combinatorial Optimization as a problem is fundamentally different from recognizing cats and dogs:
From playlist Deep Learning and Combinatorial Optimization 2021
Optimization and Tropical Combinatorics (Lecture 2) by Michael Joswig
PROGRAM COMBINATORIAL ALGEBRAIC GEOMETRY: TROPICAL AND REAL (HYBRID) ORGANIZERS Arvind Ayyer (IISc, India), Madhusudan Manjunath (IITB, India) and Pranav Pandit (ICTS-TIFR, India) DATE & TIME 27 June 2022 to 08 July 2022 VENUE Madhava Lecture Hall and Online Algebraic geometry is the stud
From playlist Combinatorial Algebraic Geometry: Tropical and Real (HYBRID)
Stefano Gualandi: "Discrete Optimal Transport by Parallel Network Simplex"
Deep Learning and Combinatorial Optimization 2021 "Discrete Optimal Transport by Parallel Network Simplex" Stefano Gualandi - Università di Pavia Abstract: We present recent results on the solution of problems related to the theory of Optimal Transport by using an efficient parallel impl
From playlist Deep Learning and Combinatorial Optimization 2021
Xavier Bresson: "The Transformer Network for the Traveling Salesman Problem"
Deep Learning and Combinatorial Optimization 2021 "The Transformer Network for the Traveling Salesman Problem" Xavier Bresson - Nanyang Technological University, Singapore Abstract: The Traveling Salesman Problem (TSP) is the most popular and most studied combinatorial problem, starting
From playlist Deep Learning and Combinatorial Optimization 2021
Petar Veličković: "Reasoning on Natural Inputs"
Deep Learning and Combinatorial Optimization 2021 "Reasoning on Natural Inputs" Petar Veličković - DeepMind Technologies Abstract: Classical algorithms are designed with abstraction in mind, enforcing their inputs to conform to stringent preconditions. This is done for an apparent reason
From playlist Deep Learning and Combinatorial Optimization 2021
Masayuki Ohzeki: "Quantum annealing and machine learning - new directions of quantum"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Quantum annealing and machine learning - new directions of quantum" Masayuki Ohzeki - Tohoku University Abstract: Quantum annealing is a generic solver of combinator
From playlist Machine Learning for Physics and the Physics of Learning 2019
Louis-Martin Rousseau: "Combining Reinforcement Learning & Constraint Programming for Combinator..."
Deep Learning and Combinatorial Optimization 2021 "Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization" Louis-Martin Rousseau - École Polytechnique de Montréal Abstract: Combinatorial optimization has found applications in numerous fields, from aero
From playlist Deep Learning and Combinatorial Optimization 2021
Emma Frejinger: "Can ML Help in Solving Cargo Capacity Management Booking Control Problems?"
Deep Learning and Combinatorial Optimization 2021 "Can ML Help in Solving Cargo Capacity Management Booking Control Problems?" Emma Frejinger - University of Montreal Abstract: Revenue management is important for carriers (e.g., airlines and railroads). In this talk we focus on cargo cap
From playlist Deep Learning and Combinatorial Optimization 2021
Yusuke Kobayashi: A weighted linear matroid parity algorithm
The lecture was held within the framework of the follow-up workshop to the Hausdorff Trimester Program: Combinatorial Optimization. Abstract: The matroid parity (or matroid matching) problem, introduced as a common generalization of matching and matroid intersection problems, is so gener
From playlist Follow-Up-Workshop "Combinatorial Optimization"