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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 551560 of 1277 papers

TitleStatusHype
A Graph Multi-separator Problem for Image Segmentation0
Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization0
Large Language Models for Supply Chain Optimization0
Explainable quantum regression algorithm with encoded data structure0
Learning to Branch in Combinatorial Optimization with Graph Pointer Networks0
A Formal Perspective on Byte-Pair EncodingCode0
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and ApplicationsCode0
Object Detection based on the Collection of Geometric Evidence0
TreeDQN: Learning to minimize Branch-and-Bound treeCode0
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss0
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