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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 576–600 of 1277 papers

TitleStatusHype
Differentially Private Partial Set Cover with Applications to Facility Location—0
Bayesian Optimization for Macro Placement—0
Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization ProblemsCode0
Neural Topological Ordering for Computation Graphs—0
Unsupervised Learning for Combinatorial Optimization with Principled Objective RelaxationCode1
Reinforcement Learning Assisted Recursive QAOACode0
Simulation-guided Beam Search for Neural Combinatorial OptimizationCode1
Joint Ranging and Phase Offset Estimation for Multiple Drones using ADS-B Signatures—0
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
Attention Round for Post-Training Quantization—0
A conditional gradient homotopy method with applications to Semidefinite Programming—0
Learning the Quality of Machine Permutations in Job Shop Scheduling—0
The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems—0
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations—0
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization—0
Learning to Control Local Search for Combinatorial OptimizationCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features—0
The Influence of Local Search over Genetic Algorithms with Balanced Representations—0
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms—0
Diffusion models as plug-and-play priorsCode2
Concentration of Data Encoding in Parameterized Quantum Circuits—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems—0
Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction—0
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