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The ESA NLP solver WORHP. (English) Zbl 1365.90007
Fasano, Giorgio (ed.) et al., Modeling and optimization in space engineering. New York, NY: Springer (ISBN 978-1-4614-4468-8/hbk; 978-1-4614-4469-5/ebook). Springer Optimization and Its Applications 73, 85-110 (2013).
Summary: 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.
For the entire collection see [Zbl 1255.90006].

90-04 Software, source code, etc. for problems pertaining to operations research and mathematical programming
90C06 Large-scale problems in mathematical programming
90C33 Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming)
90C55 Methods of successive quadratic programming type
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