Sequential quadratic programming (SQP) is a class of algorithms for solving non-linear optimization problems (NLP) in the real world. It is powerful enough for real problems because it can handle any degree of non-linearity including non-linearity in the constraints. The main disadvantage is that the method incorporates several derivatives, which likely need to be worked analytically in advance of iterating to a solution, so SQP becomes quite cumbersome for large problems with many variables or constraints. The method dates back to 1963 and was developed and refined in the 1970's .[1] SQP combines two fundamental algorithms for solving non-linear optimization problems: an active set method and Newton’s method, both of which are explained briefly below. Previous exposure to the component methods as well as to Lagrangian multipliers and Karush-Kuhn-Tucker (KKT) conditions is helpful in understanding SQP.

Last modified

• 11/30/2018

Creator

• Ben Goodman

DOI

• https://doi.org/10.21985/N25X71

Keyword

• Sequential Quadratic Programming

Rights statement

• In Copyright