sqp: (Sequential) Quadratic Programming

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.

Version: 0.5
Imports: Rcpp (≥ 1.0.0), Matrix, Rdpack
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
Published: 2020-03-31
Author: Simon Lenau
Maintainer: Simon Lenau <lenau at uni-trier.de>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: C++11, GNU Make
CRAN checks: sqp results


Reference manual: sqp.pdf
Package source: sqp_0.5.tar.gz
Windows binaries: r-devel: sqp_0.5.zip, r-devel-UCRT: sqp_0.5.zip, r-release: sqp_0.5.zip, r-oldrel: sqp_0.5.zip
macOS binaries: r-release (arm64): sqp_0.5.tgz, r-release (x86_64): sqp_0.5.tgz, r-oldrel: sqp_0.5.tgz


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