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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 11311140 of 1277 papers

TitleStatusHype
Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits0
Digging Deeper: Operator Analysis for Optimizing Nonlinearity of Boolean Functions0
Directed percolation and numerical stability of simulations of digital memcomputing machines0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems0
Discrepancy-based Evolutionary Diversity Optimization0
Discrete graphical models -- an optimization perspective0
Diseño e implementación de una meta-heurística multi-poblacional de optimización combinatoria enfocada a la resolución de problemas de asignación de rutas a vehículos0
Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing0
Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations0
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