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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 626–650 of 1277 papers

TitleStatusHype
Optimizing Tensor Network Contraction Using Reinforcement Learning—0
optimizn: a Python Library for Developing Customized Optimization Algorithms—0
Oracle Efficient Algorithms for Groupwise Regret—0
PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching—0
Parallel Genetic Algorithm to Solve Traveling Salesman Problem on MapReduce Framework using Hadoop Cluster—0
Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization—0
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity—0
Parameterized Complexity Analysis of Randomized Search Heuristics—0
Parameter Learning for Log-supermodular Distributions—0
Pareto Optimization with Robust Evaluation for Noisy Subset Selection—0
Particle Swarm Optimization: Fundamental Study and its Application to Optimization and to Jetty Scheduling Problems—0
Performance Analysis of Meta-heuristic Algorithms for a Quadratic Assignment Problem—0
Application of Monte Carlo Tree Search in Periodic Schedule Design for Networked Control Systems—0
Permutation Picture of Graph Combinatorial Optimization Problems—0
PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling—0
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems—0
Planning of Heuristics: Strategic Planning on Large Language Models with Monte Carlo Tree Search for Automating Heuristic Optimization—0
Pointer Networks Trained Better via Evolutionary Algorithms—0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
Policies for the Dynamic Traveling Maintainer Problem with Alerts—0
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization—0
Population-specific design of de-immunized protein biotherapeutics—0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
Potts model, parametric maxflow and k-submodular functions—0
Power of human-algorithm collaboration in solving combinatorial optimization problems—0
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