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Andreas Wächter
Researcher at Northwestern University
Publications - 57
Citations - 12343
Andreas Wächter is an academic researcher from Northwestern University. The author has contributed to research in topics: Nonlinear programming & Interior point method. The author has an hindex of 22, co-authored 52 publications receiving 10283 citations. Previous affiliations of Andreas Wächter include IBM & Carnegie Mellon University.
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On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
TL;DR: A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
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An algorithmic framework for convex mixed integer nonlinear programs
Pierre Bonami,Lorenz T. Biegler,Andrew R. Conn,Gérard Cornuéjols,Ignacio E. Grossmann,Carl D. Laird,Jon Lee,Andrea Lodi,François Margot,Nicolas Sawaya,Andreas Wächter +10 more
TL;DR: A class of hybrid algorithms, of which branch-and-bound and polyhedral outer approximation are the two extreme cases, are proposed and implemented and Computational results that demonstrate the effectiveness of this framework are reported.
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Branching and bounds tighteningtechniques for non-convex MINLP
TL;DR: An sBB software package named couenne (Convex Over- and Under-ENvelopes for Non-linear Estimation) is developed and used for extensive tests on several combinations of BT and branching techniques on a set of publicly available and real-world MINLP instances and is compared with a state-of-the-art MINLP solver.
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Advances in simultaneous strategies for dynamic process optimization
TL;DR: An improved algorithm for simultaneous strategies for dynamic optimization based on interior point methods is developed and a reliable and efficient algorithm to adjust elements to track optimal control profile breakpoints and to ensure accurate state and control profiles is developed.
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Line Search Filter Methods for Nonlinear Programming: Motivation and Global Convergence
TL;DR: Under mild assumptions it is shown that every limit point of the sequence of iterates generated by the algorithm is feasible, and that there exists at least one limit point that is a stationary point for the problem under consideration.