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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 321330 of 1277 papers

TitleStatusHype
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection0
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy0
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
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs0
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks0
Addressing The Knapsack Challenge Through Cultural Algorithm Optimization0
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning0
Digging Deeper: Operator Analysis for Optimizing Nonlinearity of Boolean Functions0
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