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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 851860 of 1277 papers

TitleStatusHype
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
POMO: Policy Optimization with Multiple Optima for Reinforcement LearningCode1
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity0
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle RoutingCode1
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemsCode0
Feature Importance Ranking for Deep LearningCode1
Continuous Latent Search for Combinatorial Optimization0
Neural Large Neighborhood Search0
CoCo: Learning Strategies for Online Mixed-Integer Control0
Virtual Savant: learning for optimization0
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