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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 601–650 of 1277 papers

TitleStatusHype
On Approximation Guarantees for Greedy Low Rank Optimization—0
On Circuit Depth Scaling For Quantum Approximate Optimization—0
On combinatorial optimization for dominating sets (literature survey, new models)—0
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models—0
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds—0
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK—0
Online combinatorial optimization with stochastic decision sets and adversarial losses—0
Online learning for min-max discrete problems—0
ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers—0
On permutation symmetries in Bayesian neural network posteriors: a variational perspective—0
On Size Generalization in Graph Neural Networks—0
From Local Structures to Size Generalization in Graph Neural Networks—0
On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments—0
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization—0
On the Generalization of Neural Combinatorial Optimization Heuristics—0
On the Mathematical Relationship between Expected n-call@k and the Relevance vs. Diversity Trade-off—0
On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems—0
On the Performance of Metaheuristics: A Different Perspective—0
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling—0
On the String Kernel Pre-Image Problem with Applications in Drug Discovery—0
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks—0
Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction—0
Optimization Networks for Integrated Machine Learning—0
Optimized Online Rank Learning for Machine Translation—0
Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections—0
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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