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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 1201–1225 of 1277 papers

TitleStatusHype
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
Extended Deep Submodular Functions—0
Fair Disaster Containment via Graph-Cut Problems—0
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method—0
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization—0
Faster Matchings via Learned Duals—0
Faster quantum mixing for slowly evolving sequences of Markov chains—0
Faster width-dependent algorithm for mixed packing and covering LPs—0
Fast Hyperparameter Tuning for Ising Machines—0
Fast instance-specific algorithm configuration with graph neural network—0
Feature Selection for Classification with QAOA—0
Feature subset selection for Big Data via Chaotic Binary Differential Evolution under Apache Spark—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Feeder Load Balancing using Neural Network—0
Fewer Truncations Improve Language Modeling—0
Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem—0
Finding Support Examples for In-Context Learning—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
First-order regret bounds for combinatorial semi-bandits—0
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search—0
Fixed Priority Global Scheduling from a Deep Learning Perspective—0
Focused Jump-and-Repair Constraint Handling for Fixed-Parameter Tractable Graph Problems Closed Under Induced Subgraphs—0
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
Fragmentation trees reloaded—0
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