conjugate gradient descent
Conjugate gradient descent is an optimization algorithm used to find the minimum value of a mathematical function by iteratively updating a search direction and step length in a way that efficiently minimizes the function. It performs a series of conjugate steps, where each step is a linear combination of the function's gradient and the previous search direction. This method is particularly useful for solving large scale optimization problems.
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