Which algorithm is most suitable for solving optimization problems in Numerical and Statistical Computing?

Gradient Descent is a widely used optimization algorithm in numerical and statistical computing. It is designed to find the minimum of a function by iteratively moving in the direction of the steepest descent, as defined by the negative gradient. This algorithm is essential for training various machine learning models, especially those involving optimization problems where the goal is to minimize a cost or loss function. Why Other Options are Wrong: b) K-Means Clustering is used for clustering data rather than optimization. c) Decision Trees are used for classification and regression tasks, not for optimization problems. d) Genetic Algorithms are heuristic search algorithms inspired by natural selection and can solve optimization problems but are not as widely used as Gradient Descent for many numerical problems. e) Support Vector Machines (SVM) are used for classification and regression tasks, not specifically for solving optimization problems.