Combinatorial Optimization

111 papers with code • 0 benchmarks • 1 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.

Source: Recent Advances in Neural Program Synthesis


Greatest papers with code

ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution

google-research/google-research 19 Jan 2021

We consider the problem of efficient blackbox optimization over a large hybrid search space, consisting of a mixture of a high dimensional continuous space and a complex combinatorial space.

Combinatorial Optimization Continuous Control +3

Fair Correlation Clustering

google-research/google-research 6 Feb 2020

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.

Combinatorial Optimization Fairness

Pointer Networks

PaddlePaddle/models NeurIPS 2015

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.

Combinatorial Optimization

Attention, Learn to Solve Routing Problems!

wouterkool/attention-tsp ICLR 2019

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

Combinatorial Optimization

Learning Combinatorial Optimization Algorithms over Graphs

Hanjun-Dai/graph_comb_opt NeurIPS 2017

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

Combinatorial Optimization Graph Embedding

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

crazydonkey200/neural-symbolic-machines NeurIPS 2018

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.

Combinatorial Optimization Program Synthesis +2

Causal Discovery with Reinforcement Learning

huawei-noah/trustworthyAI ICLR 2020

The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.

Causal Discovery Combinatorial Optimization

Reinforcement Learning for Solving the Vehicle Routing Problem

OptMLGroup/VRP-RL NeurIPS 2018

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.

Combinatorial Optimization