Quasi-Newton Methods (QNMs) are generally a class of optimization methods that are used in Non-Linear Programming when full Newton’s Methods are either too time consuming or difficult to use. More specifically, these methods are used to find the global minimum of a function f(x) that is twice-differentiable. There are distinct advantages to using Quasi-Newton Methods over the full Newton's Method for expansive and complex non-linear problems. These methods are not perfect, however, and can have some drawbacks depending on the exact type of Quasi-Newton Method used and the problem to which it is applied. Despite this, Quasi-Newton Methods are generally worth using with the exception of very simple problems.

Last modified

• 11/30/2018

Creator

• Vincent Cericola

DOI

• https://doi.org/10.21985/N2FF2S

Keyword

• Quasi-Newton

Rights statement

• In Copyright