Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes
SIAM, 1 ene 2010 - 415 páginas
This book addresses modern nonlinear programming (NLP) concepts and algorithms, especially as they apply to challenging applications in chemical process engineering. The author provides a firm grounding in fundamental NLP properties and algorithms, and relates them to real-world problem classes in process optimization, thus making the material understandable and useful to chemical engineers and experts in mathematical optimization.
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active set adjoint algorithm applied approach approximation BFGS BFGS update bound calculation Chapter Chem Comput consider constrained optimization control proﬁles convergence properties convex DAE system deﬁne developed differential difﬁcult discretized efﬁcient eigenvalues equality constraints Example Figure ﬁlter ﬁnal ﬁnd ﬁnite elements ﬁxed ﬂow rate global Hessian matrix inequality constraints initial interior point methods IPOPT iteration KKT conditions KKT matrix KNITRO L. T. Biegler large-scale leads LICQ line search Lipschitz continuous merit function model predictive control modiﬁed Moreover MPCC multiple shooting multipliers Newton step NLP formulation NLP solver nonlinear program nonsingular objective function optimal control optimal control problem optimal solution optimality conditions parameter polynomial positive deﬁnite process optimization quasi-Newton reactor reduced Hessian reduced-space reformulation requires result satisﬁes second derivatives second order conditions Section SNOPT solve speciﬁed strategies subproblem superbasic Theorem tray trust region trust region methods unconstrained values vector