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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 471480 of 1277 papers

TitleStatusHype
Fuzzy Integer Linear Programming Mathematical Models for Examination Timetable Problem0
Combinatorial optimization and reasoning with graph neural networks0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem0
Faster Matchings via Learned Duals0
Deep Generative Model for Mechanical System Configuration Design0
Faster width-dependent algorithm for mixed packing and covering LPs0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms0
Fast Hyperparameter Tuning for Ising Machines0
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks0
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem0
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