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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 826–850 of 1277 papers

TitleStatusHype
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
Solving Graph-based Public Good Games with Tree Search and Imitation LearningCode0
An SMT Based Compositional Algorithm to Solve a Conflict-Free Electric Vehicle Routing Problem—0
Learning Pseudo-Backdoors for Mixed Integer Programs—0
Fair Disaster Containment via Graph-Cut Problems—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Experiments with graph convolutional networks for solving the vertex p-center problem—0
Policies for the Dynamic Traveling Maintainer Problem with Alerts—0
On a class of data-driven mixed-integer programming problems under uncertainty: a distributionally robust approach—0
Structural Causal Models Reveal Confounder Bias in Linear Program ModellingCode0
IA-GM: A Deep Bidirectional Learning Method for Graph Matching—0
Graph Learning: A Survey—0
Reconstruction of Convex Polytope Compositions from 3D Point-clouds—0
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble LearningCode0
Exact and Approximate Hierarchical Clustering Using A*—0
Ecole: A Library for Learning Inside MILP SolversCode0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems—0
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems—0
quantum Case-Based Reasoning (qCBR)—0
Greedy-Based Feature Selection for Efficient LiDAR SLAM—0
Leveraging Conflicting Constraints in Solving Vehicle Routing Problems—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise ConstraintsCode0
Knowledge engineering mixed-integer linear programming: constraint typology—0
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