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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 451460 of 1277 papers

TitleStatusHype
Finding Support Examples for In-Context Learning0
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology0
Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem0
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms0
Experiments with graph convolutional networks for solving the vertex p-center problem0
Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application0
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem0
Deep Generative Model for Mechanical System Configuration Design0
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