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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 331–340 of 1277 papers

TitleStatusHype
Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations—0
Deep Reinforcement Learning for Traveling Purchaser Problems—0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer—0
Deep Reinforcement Learning for Modelling Protein Complexes—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
DeepSimplex: Reinforcement Learning of Pivot Rules Improves the Efficiency of Simplex Algorithm in Solving Linear Programming Problems—0
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs—0
A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based Approaches for E-GTSP—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
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