(Sequential) Quadratic Programming
Overview
Solving procedures for quadratic programming with optional equality and inequality constraints, which can be used for by sequential quadratic programming (SQP). Similar to Newton-Raphson methods in the unconstrained case, sequential quadratic programming solves non-linear constrained optimization problems by iteratively solving linear approximations of the optimality conditions of such a problem (cf. Powell (1978) doi:10.1007/BFb0067703; Nocedal and Wright (1999, ISBN: 978-0-387-98793-4)). The Hessian matrix in this strategy is commonly approximated by the BFGS method in its damped modification proposed by Powell (1978) doi:10.1007/BFb0067703. All methods are implemented in C++ as header-only library, such that it is easy to use in other packages.
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People & History
- RR 4.2.0 released · 2022-04-22
- archivedRemoved from CRAN2021-12-20email to the maintainer is undeliverable
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.52020-03-31
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-03-31
- Total releases
- 1 / 6 yrs
- License
- GPL-3 OSI
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