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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 12511260 of 1277 papers

TitleStatusHype
Prompt Learning for Generalized Vehicle RoutingCode0
Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural NetworksCode0
Protecting Geolocation Privacy of Photo CollectionsCode0
Multi-objective Pointer Network for Combinatorial OptimizationCode0
XX^t Can Be FasterCode0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded SizeCode0
Multi-Robot Connected Fermat Spiral CoverageCode0
The Exact Solution to Rank-1 L1-norm TUCKER2 DecompositionCode0
Evaluate Quantum Combinatorial Optimization for Distribution Network ReconfigurationCode0
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