add gradient_descent algorithm
#580
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Description
This code contribution introduces a Rust function for implementing gradient descent optimization. Gradient descent is an iterative algorithm used to minimize a mathematical function. This function is designed to work with both univariate (single numeric value) and multivariate (multiple numeric values) functions represented as f64 data types.
Inside the function, it repeatedly updates the values in the x vector in a direction that minimizes the function's value. The update equation for each value in x is given as:
x_{k+1} = x_k - learning_rate * gradient_of_function(x_k).To ensure the correctness of the optimization process, the code includes test cases that check whether the final optimized vector matches the expected results, with a specified tolerance for small differences. This provides a way to validate that the optimization process is functioning correctly when applied to various functions. Covers #578