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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 901910 of 1277 papers

TitleStatusHype
Local Branching Relaxation Heuristics for Integer Linear Programs0
Local Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with Correlated Objectives0
Local Perturb-and-MAP for Structured Prediction0
New Characterizations and Efficient Local Search for General Integer Linear Programming0
Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization0
Long Term Memory Network for Combinatorial Optimization Problems0
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization0
LRM-1B: Towards Large Routing Model0
Machine Learning and Constraint Programming for Efficient Healthcare Scheduling0
Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems0
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