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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 621630 of 1277 papers

TitleStatusHype
Design Space Exploration as Quantified Satisfaction0
Design And Optimization Of Multi-rendezvous Manoeuvres Based On Reinforcement Learning And Convex Optimization0
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations0
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint0
Density Maximization in Context-Sense Metric Space for All-words WSD0
Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design0
A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based Approaches for E-GTSP0
Analysis of Quality Diversity Algorithms for the Knapsack Problem0
DeepSimplex: Reinforcement Learning of Pivot Rules Improves the Efficiency of Simplex Algorithm in Solving Linear Programming Problems0
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs0
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