NonLinear Programming (NLP): Sequential quadratic programming

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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
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