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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 391400 of 1277 papers

TitleStatusHype
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems0
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry0
Deep Learning of Graph Matching0
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules0
Effective anytime algorithm for multiobjective combinatorial optimization problems0
Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method0
Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry0
Attention Round for Post-Training Quantization0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training0
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems0
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