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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 491500 of 1277 papers

TitleStatusHype
Graph Neural Networks for Job Shop Scheduling Problems: A Survey0
First-order regret bounds for combinatorial semi-bandits0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models0
Fixed Priority Global Scheduling from a Deep Learning Perspective0
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems0
DeepDA: LSTM-based Deep Data Association Network for Multi-Targets Tracking in Clutter0
A topological analysis of the space of recipes0
DeepCO: Offline Combinatorial Optimization Framework Utilizing Deep Learning0
Combinatorial Persistency Criteria for Multicut and Max-Cut0
Deep Causal Learning: Representation, Discovery and Inference0
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