An Implementation of Locally Feasibly Projected Sequential Quadratic Programming | AIChE

An Implementation of Locally Feasibly Projected Sequential Quadratic Programming

Type

Conference Presentation

Conference Type

AIChE Annual Meeting

Presentation Date

November 10, 2021

Duration

19 minutes

Skill Level

Intermediate

PDHs

0.50

Nonlinear optimization algorithms that only take feasible steps are sometimes desirable. For example, objective functions may not be defined outside of the domain determined by the constraints. Feasible steps also eliminate the need for specially constructed merit functions. Using theory established for optimization on Riemannian manifolds, we have developed a general high-performance numerical code for feasible sequential quadratic programming with smooth nonlinear equality and/or inequality constraints. Each step of the algorithm is dominated by an O(Nm2)-flop factorization, where N is the number of variables and m is the number of constraints. Subroutines for retractions and fast truncated Newton steps that exploit automatic differentiation and Krylov subspace methods will be discussed.

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