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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 591600 of 1277 papers

TitleStatusHype
Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks0
Diseño e implementación de una meta-heurística multi-poblacional de optimización combinatoria enfocada a la resolución de problemas de asignación de rutas a vehículos0
Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints0
An efficient algorithm for learning with semi-bandit feedback0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks0
Accelerating Matroid Optimization through Fast Imprecise Oracles0
Discrete graphical models -- an optimization perspective0
Discrepancy-based Evolutionary Diversity Optimization0
An Efficient Algorithm for Cooperative Semi-Bandits0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems0
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