kvasikohandamismeetodid
Kvasikohandamismeetodid, also known as quasi-Newton methods, are iterative algorithms used in numerical optimization to find the minimum of a function. These methods approximate the Hessian matrix, which contains second-order partial derivatives of the objective function, rather than calculating it directly. This approximation is updated at each iteration, making the methods more efficient than traditional Newton's method, especially for functions where computing the Hessian is computationally expensive or impossible.
The core idea behind quasi-Newton methods is to use the gradient information from previous steps to build
Quasi-Newton methods are widely applied in various fields, including machine learning, econometrics, and engineering, due to