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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 471–480 of 1277 papers

TitleStatusHype
Large Scale Constrained Clustering With Reinforcement Learning—0
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution ShiftsCode0
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization—0
Assortment Planning with Sponsored Products—0
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
Effective anytime algorithm for multiobjective combinatorial optimization problems—0
torchmSAT: A GPU-Accelerated Approximation To The Maximum Satisfiability Problem—0
Accelerating Matroid Optimization through Fast Imprecise Oracles—0
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems—0
Manipulating Predictions over Discrete Inputs in Machine Teaching—0
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