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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 451460 of 1277 papers

TitleStatusHype
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetsCode1
The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem0
Neural Bee Colony Optimization: A Case Study in Public Transit Network Design0
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits0
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
Monotone comparative statics for submodular functions, with an application to aggregated deferred acceptance0
Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated AnnealingCode0
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