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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 10911100 of 1277 papers

TitleStatusHype
Recent Progress on Graph Partitioning Problems Using Evolutionary Computation0
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem0
Regularized Greedy Column Subset Selection0
Composing photomosaic images using clustering based evolutionary programming0
Attention, Learn to Solve Routing Problems!Code1
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection0
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form0
Cakewalk Sampling0
Memcomputing: Leveraging memory and physics to compute efficiently0
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