Combinatorial Optimization
291 papers with code • 0 benchmarks • 2 datasets
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.
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Latest papers
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
feiliu36/mtnco
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The results show that the unified model demonstrates superior performance in the eleven VRPs, reducing the average gap to around 5% from over 20% in the existing approach and achieving a significant performance boost on benchmark datasets as well as a real-world logistics application.
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts
tumbais/risksensitivesacforrobustdrlunderdistshifts
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We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimization problems from the operations research domain.
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
timd3/moco
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Our approach, Moco, learns a graph neural network that updates the solution construction procedure based on features extracted from the current search state.
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
ai4co/llm-as-hh
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The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design process.
Domain-Independent Dynamic Programming
We experimentally compare our DIDP solvers with commercial MIP and CP solvers (solving MIP and CP models, respectively) on common benchmark instances of eleven combinatorial optimization problem classes.
Simulation Based Bayesian Optimization
BO constructs a probabilistic surrogate model of the objective function given the covariates, which is in turn used to inform the selection of future evaluation points through an acquisition function.
OsmLocator: locating overlapping scatter marks with a non-training generative perspective
In addition, we especially built a dataset named SML2023 containing hundreds of scatter images with different markers and various levels of overlapping severity, and tested the proposed method and compared it to existing methods.
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
Combinatorial Optimization (CO) problems over graphs appear routinely in many applications such as in optimizing traffic, viral marketing in social networks, and matching for job allocation.
Solving the Team Orienteering Problem with Transformers
danifuertes/top_transformer
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This problem is usually modeled as a Combinatorial Optimization problem named as Team Orienteering Problem.
A Survey and Analysis of Evolutionary Operators for Permutations
There are many combinatorial optimization problems whose solutions are best represented by permutations.
