The proposed model is based on the stochastic fractal search (SFS) – a powerful metaheuristic – and a nature-inspired algorithm that is claimed to solve complex optimization problems.

As Matthew Hoffman, a coauthor of both papers, now a senior research scientist at Adobe Research explains, "Stochastic optimization algorithms often find a good solutions before they've even ...

A novel parallel decomposition algorithm is developed for large, multistage stochastic optimization problems. The method decomposes the problem into subproblems that correspond to scenarios. The ...

We consider the vehicle routing problem with stochastic demands (VRPSD). We give randomized approximation algorithms achieving approximation guarantees of 1 + α for split-delivery VRPSD, and 2 + α for ...

Furthermore, gradient information is very much unavailable or is spurious. Stochastic optimization algorithms are even more suitable when it comes to coming up with the global optimum solution.

Mathematics Colloquium: Stochastic Variance-Reduced Majorization-Minimization Algorithms, Duy Nhat Phan 4/26 Submissions 04/20/2023 By Joris Roos The Department of Mathematics & Statistics, Kennedy ...

The Data Science Lab Differential Evolution Optimization Dr. James McCaffrey of Microsoft Research explains stochastic gradient descent (SGD) neural network training, specifically implementing a ...