Book ChapterDOI
The ESA NLP Solver WORHP
Christof Büskens,Dennis Wassel +1 more
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TLDR
Two large-scale optimization problems from space applications that demonstrate the robustness of the solver complement the cursory description of general NLP methods and some WORHP implementation details.Abstract:
We Optimize Really Huge Problems (WORHP) is a solver for large-scale, sparse, nonlinear optimization problems with millions of variables and constraints. Convexity is not required, but some smoothness and regularity assumptions are necessary for the underlying theory and the algorithms based on it. WORHP has been designed from its core foundations as a sparse sequential quadratic programming (SQP) / interior-point (IP) method; it includes efficient routines for computing sparse derivatives by applying graph-coloring methods to finite differences, structure-preserving sparse named after Broyden, Fletcher, Goldfarb and Shanno (BFGS) update techniques for Hessian approximations, and sparse linear algebra. Furthermore it is based on reverse communication, which offers an unprecedented level of interaction between user and nonlinear programming (NLP) solver. It was chosen by ESA as the European NLP solver on the basis of its high robustness and its application-driven design and development philosophy. Two large-scale optimization problems from space applications that demonstrate the robustness of the solver complement the cursory description of general NLP methods and some WORHP implementation details.read more
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The SCIP Optimization Suite 7.0
Gerald Gamrath,Daniel Anderson,Ksenia Bestuzheva,Wei-Kun Chen,Leon Eifler,Maxime Gasse,Patrick Gemander,Ambros M. Gleixner,Leona Gottwald,Katrin Halbig,Gregor Hendel,Christopher Hojny,Thorsten Koch,Pierre Le Bodic,Stephen J. Maher,Frederic Matter,Matthias Miltenberger,Erik Muhmer,Benjamin Müller,Marc E. Pfetsch,Franziska Schlösser,Felipe Serrano,Yuji Shinano,Christine Maher Fouad Tawfik,Stefan Vigerske,Fabian Wegscheider,Dieter Weninger,Jakob Witzig +27 more
TL;DR: New features and enhanced algorithms made available in version 5.0 of the SCIP Optimization Suite, in particular for the LP solver SoPlex, the Steiner tree solver SCIP-Jack, the MISDP solverSCIP-SDP, and the parallelization framework UG are described.
Journal ArticleDOI
All you need to know about model predictive control for buildings
Ján Drgoňa,Ján Drgoňa,Javier Arroyo,Iago Cupeiro Figueroa,David Blum,Krzysztof Arendt,Donghun Kim,Donghun Kim,Enric Perarnau Ollé,Juraj Oravec,Michael Wetter,Draguna Vrabie,Lieve Helsen +12 more
TL;DR: This paper provides a unified framework for model predictive building control technology with focus on the real-world applications and presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems.
Journal ArticleDOI
Minimizing the levelized cost of electricity production from low-temperature geothermal heat sources with ORCs: Water or air cooled?
TL;DR: In this article, a system optimization of ORCs cooled by air-cooled condensers or wet cooling towers and powered by low-temperature geothermal heat sources is performed, where the conguration of the ORC is optimized together with the geometry of all the components.
Journal ArticleDOI
Economic system optimization of air-cooled organic Rankine cycles powered by low-temperature geothermal heat sources
TL;DR: In this paper, an economic system optimization of an air-cooled organic Rankine cycle, powered by geothermal heat, is performed to find the conguration of the ORC which gives the highest possible net present value for the project.
Journal ArticleDOI
Optimum configuration of shell-and-tube heat exchangers for the use in low-temperature organic Rankine cycles
TL;DR: In this paper, a first step towards a system optimization of organic Rankine cycles is taken by optimizing the cycle parameters together with the configuration of shell-and-tube heat exchangers.
References
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Book
Practical Methods for Optimal Control Using Nonlinear Programming
John T. Betts,I Kolmanovsky +1 more
TL;DR: The optimal control problem is illustrated with examples of large, sparse nonlinear programming and a comparison of optimal control problems in the context of discrete-time programming.
Journal ArticleDOI
The Fritz John Necessary Optimality Conditions in the Presence of Equality and Inequality Constraints
Olvi L. Mangasarian,S Fromovitz +1 more
TL;DR: In this article, the Kuhn-Tucker criterion was extended to the case of equalities and inequalities, and a new generalization of the Fritz-John criterion was proposed.
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