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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 581590 of 1277 papers

TitleStatusHype
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms0
Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machine0
Graph Neural Networks for the Offline Nanosatellite Task Scheduling ProblemCode0
Training Deep Boltzmann Networks with Sparse Ising Machines0
Redrawing attendance boundaries to promote racial and ethnic diversity in elementary schools0
Material Identification From Radiographs Without Energy Resolution0
Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks, vol. 122, pp. 106085, 2023Code0
Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time WindowsCode0
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits0
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization0
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