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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 51–75 of 1277 papers

TitleStatusHype
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows—0
UniCO: Towards a Unified Model for Combinatorial Optimization Problems—0
Primal-dual algorithm for contextual stochastic combinatorial optimization—0
Unraveling the Rainbow: can value-based methods schedule?Code0
Entropy-Guided Sampling of Flat Modes in Discrete SpacesCode0
Integrating Column Generation and Large Neighborhood Search for Bus Driver Scheduling with Complex Break Constraints—0
Learning to Learn with Quantum Optimization via Quantum Neural Networks—0
QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks—0
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search—0
Application of the Brain Drain Optimization Algorithm to the N-Queens Problem—0
QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis—0
PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling—0
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach—0
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
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
Futureproof Static Memory PlanningCode0
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
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
Neural Combinatorial Optimization for Real-World RoutingCode1
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