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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

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Showing 451–475 of 1277 papers

TitleStatusHype
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems—0
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks—0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology—0
Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning—0
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise—0
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms—0
Experiments with graph convolutional networks for solving the vertex p-center problem—0
Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application—0
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem—0
CoCo: Learning Strategies for Online Mixed-Integer Control—0
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem—0
Deep Generative Model for Mechanical System Configuration Design—0
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning—0
Fair Disaster Containment via Graph-Cut Problems—0
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs—0
A General Large Neighborhood Search Framework for Solving Integer Linear Programs—0
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method—0
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization—0
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks—0
Combinatorial optimization and reasoning with graph neural networks—0
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem—0
Faster Matchings via Learned Duals—0
Fragmentation trees reloaded—0
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