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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

Showing 641650 of 1277 papers

TitleStatusHype
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems0
Planning of Heuristics: Strategic Planning on Large Language Models with Monte Carlo Tree Search for Automating Heuristic Optimization0
Pointer Networks Trained Better via Evolutionary Algorithms0
Pointer Networks with Q-Learning for Combinatorial Optimization0
Policies for the Dynamic Traveling Maintainer Problem with Alerts0
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization0
Population-specific design of de-immunized protein biotherapeutics0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks0
Potts model, parametric maxflow and k-submodular functions0
Power of human-algorithm collaboration in solving combinatorial optimization problems0
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