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Found 9 papers, 7 papers with code
PyVRP: a high-performance VRP solver package
We hope that PyVRP enables researchers and practitioners to easily and quickly build on a state-of-the-art VRP solver.
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities.
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts
Training large-scale mixture of experts models efficiently on modern hardware requires assigning datapoints in a batch to different experts, each with a limited capacity.
Deep Policy Dynamic Programming for Vehicle Routing Problems
Routing problems are a class of combinatorial problems with many practical applications.
Estimating Gradients for Discrete Random Variables by Sampling without Replacement
We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples.
Buy 4 REINFORCE Samples, Get a Baseline for Free!
REINFORCE can be used to train models in structured prediction settings to directly optimize the test-time objective.
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search.
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.
