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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 1026–1050 of 1277 papers

TitleStatusHype
Causal Discovery with Reinforcement Learning—0
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem—0
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process—0
Combinatorial Persistency Criteria for Multicut and Max-Cut—0
Two-Dimensional Phase Unwrapping via Balanced Spanning Forests—0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning—0
Solving NP-Hard Problems on Graphs with Extended AlphaGo ZeroCode0
Exact-K Recommendation via Maximal Clique OptimizationCode0
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial OptimizationCode0
An LP-Based Approach for Goal Recognition as Planning—0
Design Space Exploration as Quantified Satisfaction—0
A new dog learns old tricks: RL finds classic optimization algorithms—0
The Cakewalk Method—0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach—0
Training Hard-Threshold Networks with Combinatorial Search in a Discrete Target Propagation Setting—0
Balanced Crossover Operators in Genetic AlgorithmsCode0
Structural Self-adaptation for Decentralized Pervasive Intelligence—0
Bottleneck potentials in Markov Random Fields—0
An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing—0
Addressing Model Vulnerability to Distributional Shifts over Image Transformation SetsCode0
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem—0
Dynamic Learning of Sequential Choice Bandit Problem under Marketing FatigueCode0
Iterated two-phase local search for the Set-Union Knapsack Problem—0
Learning Self-Game-Play Agents for Combinatorial Optimization Problems—0
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