Monte Carlo Algorithm with MATLAB Implementation
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This article presents several MATLAB programs implementing Monte Carlo algorithms that can be directly utilized for computational tasks. The Monte Carlo algorithm is a probability-based computational method that solves various mathematical problems including integration, differential equations, and optimization challenges. The core methodology involves generating large quantities of random samples to approximate solutions to complex problems through statistical sampling techniques. These MATLAB implementations typically leverage built-in functions like rand() and randn() for random number generation, and employ vectorized operations for efficient large-scale simulations. Monte Carlo algorithms find extensive applications in finance, physics, and engineering sectors, serving as powerful mathematical tools for stochastic modeling and numerical approximation where analytical solutions are impractical or impossible to obtain.
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