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# Combinatorial Optimization Edit

75 papers with code · Methodology

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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# Fair Correlation Clustering

We define a fairlet decomposition with cost similar to the $k$-median cost and this allows us to obtain approximation algorithms for a wide range of fairness constraints.

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# Pointer Networks

It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output.

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# Attention, Learn to Solve Routing Problems!

The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development.

406

# Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.

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# Learning Combinatorial Optimization Algorithms over Graphs

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.

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# Reinforcement Learning for Solving the Vehicle Routing Problem

Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance.

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# Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications

14 Nov 2016bjoern-andres/graph

In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.

183

# Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

25 Mar 2020martius-lab/blackbox-backprop

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers.

172

# Local Search is State of the Art for Neural Architecture Search Benchmarks

6 May 2020naszilla/naszilla

Local search is one of the simplest families of algorithms in combinatorial optimization, yet it yields strong approximation guarantees for canonical NP-Complete problems such as the traveling salesman problem and vertex cover.

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# Neural Combinatorial Optimization with Reinforcement Learning

Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.

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