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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 631640 of 1277 papers

TitleStatusHype
Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization0
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity0
Parameterized Complexity Analysis of Randomized Search Heuristics0
Parameter Learning for Log-supermodular Distributions0
Pareto Optimization with Robust Evaluation for Noisy Subset Selection0
Particle Swarm Optimization: Fundamental Study and its Application to Optimization and to Jetty Scheduling Problems0
Performance Analysis of Meta-heuristic Algorithms for a Quadratic Assignment Problem0
Application of Monte Carlo Tree Search in Periodic Schedule Design for Networked Control Systems0
Permutation Picture of Graph Combinatorial Optimization Problems0
PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling0
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