SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 of 1277 papers

TitleStatusHype
A 10.8mW Mixed-Signal Simulated Bifurcation Ising Solver using SRAM Compute-In-Memory with 0.6us Time-to-Solution—0
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps—0
Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization—0
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application—0
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms—0
Futureproof Static Memory PlanningCode0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems—0
Unsupervised Learning for Quadratic Assignment—0
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic ProgrammingCode0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms—0
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution—0
Combinatorial Optimization via LLM-driven Iterated Fine-tuning—0
Neural Combinatorial Optimization via Preference Optimization—0
Self-Supervised Penalty-Based Learning for Robust Constrained Optimization—0
Object Packing and Scheduling for Sequential 3D Printing: a Linear Arithmetic Model and a CEGAR-inspired Optimal Solver—0
Reheated Gradient-based Discrete Sampling for Combinatorial OptimizationCode0
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization ProblemsCode0
Learning to Reduce Search Space for Generalizable Neural Routing Solver—0
A2Perf: Real-World Autonomous Agents Benchmark—0
Lattice Protein Folding with Variational Annealing—0
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization—0
optimizn: a Python Library for Developing Customized Optimization Algorithms—0
Show:102550
← PrevPage 11 of 52Next →

No leaderboard results yet.