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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 12411250 of 1277 papers

TitleStatusHype
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemsCode0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
Combinatorial optimization of the coefficient of determinationCode0
Pretrained Cost Model for Distributed Constraint Optimization ProblemsCode0
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering ProblemCode0
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
Demand Selection for VRP with Emission QuotaCode0
Exact-K Recommendation via Maximal Clique OptimizationCode0
Multidataset Independent Subspace Analysis with Application to Multimodal FusionCode0
ProDAG: Projected Variational Inference for Directed Acyclic GraphsCode0
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