Stochastic Algorithms

Stochastic algorithms are a class of algorithms that use random variables to solve optimization problems. These algorithms are particularly useful when the objective function is complex and difficult to optimize using deterministic methods. Stochastic algorithms are widely used in machine learning, data science, and artificial intelligence applications, such as deep learning, reinforcement learning, and genetic algorithms. The main advantage of stochastic algorithms is that they can escape local optima and find global optima by exploring the search space more effectively. However, stochastic algorithms are computationally expensive and require a large number of iterations to converge to a solution. Therefore, the choice of the algorithm and its parameters is critical to achieve good performance.
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