Category: Randomized Optimization

Selecting the right optimization problem is crucial for solving complex challenges, involving the adjustment of model parameters to optimize an objective function in machine learning. Mathematical and computational techniques aim to find the best solution from a set of feasible ones, focusing on objective functions, decision variables, and constraints. Optimization enhances machine learning models through training, hyperparameter tuning, feature selection, and cost function minimization, directly affecting accuracy and performance. This process necessitates an understanding of problem specifics, appropriate metric selection, and computational complexity consideration, while avoiding pitfalls like unclear objectives and overlooking real-world constraints.

Introduction Local Search Algorithms play a crucial role in Machine Learning by addressing a wide range of optimization problems, as noted by Solis and Wets [1]. These algorithms are especially useful for tasks like hyperparameter optimization or optimizing loss functions. Search algorithms are particularly beneficial in situations where computational resources are limited or the problem […]

Optimization techniques play a critical role in numerous challenges within machine learning and signal processing spaces.This blog specifically focuses on a significant class of methods for global optimization known as Simulated Annealing (SA). We cover the motivation, procedures and types of simulated annealing that have been used over the years. Finally, we look at some real world applications of simulated annealing, not limited to the realms of Machine Learning, demonstrating the power of this technique.