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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 571580 of 1277 papers

TitleStatusHype
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization0
Near-Optimal LOS and Orientation Aware Intelligent Reflecting Surface Placement0
Quantum-Based Combinatorial Optimization for Optimal Sensor Placement in Civil Structures0
New Characterizations and Efficient Local Search for General Integer Linear Programming0
Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated AnnealingCode0
Monotone comparative statics for submodular functions, with an application to aggregated deferred acceptance0
LayerNAS: Neural Architecture Search in Polynomial Complexity0
Genetic Algorithm Based Combinatorial Optimization for the Optimal Design of Water Distribution Network of Gurudeniya Service Zone, Sri Lanka0
Quantum Annealing for Single Image Super-Resolution0
RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization0
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