Transcription of Nonlinear Programming: Concepts, Algorithms and …
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Nonlinear Programming: Concepts, Algorithms and ApplicationsL. T. BieglerChemical Engineering DepartmentCarnegie Mellon UniversityPittsburgh, PA 2 IntroductionUnconstrained Optimization Algorithms Newton Methods Quasi-Newton MethodsConstrained Optimization Karush Kuhn-Tucker Conditions Special Classes of Optimization Problems Reduced Gradient Methods (GRG2, CONOPT, MINOS) Successive Quadratic Programming (SQP) Interior Point MethodsProcess Optimization Black Box Optimization Modular Flowsheet Optimization Infeasible Path The Role of Exact DerivativesLarge-Scale Nonlinear Programming Data Reconciliation Real-time Process OptimizationFurther Applications Sensitivity Analysis for NLP Solutions Multiperiod Optimization ProblemsSummary and ConclusionsNonlinear Programming and Process Optimization3 IntroductionOptimization.
• Symmetric Matrix - A ∈ℜn x n (square matrix) and A = AT • Identity Matrix - I, square matrix with ones on diagonal and zeroes elsewhere. • Determinant: "Inverse Volume" measure of a square matrix det(A) = Σi (-1)i+j Aij Aij for any j, or det(A) = Σj (-1)i+j Aij Aij for any i, where Aij is the determinant
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