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.

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 ...

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.

We propose an optimization-via-simulation algorithm, called COMPASS, for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables are ...

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

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 ...

Advised by ECE professor Andrew Teel, Crisafulli studies stochastic approximation theory applied to hybrid dynamical systems. Although theoretical, the work can be applied to establish performance ...

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 ...