| Type: | Package |
| Title: | Interface to the 'qpmad' Quadratic Programming Solver |
| Version: | 1.1.0-0 |
| Date: | 2021-06-23 |
| Description: | Efficiently solve quadratic problems with linear inequality, equality and box constraints. The method used is outlined in D. Goldfarb, and A. Idnani (1983) <doi:10.1007/BF02591962>. |
| License: | GPL (≥ 3) |
| URL: | https://github.com/anderic1/qpmadr |
| BugReports: | https://github.com/anderic1/qpmadr/issues |
| Depends: | R (≥ 3.0.2) |
| Imports: | Rcpp, checkmate |
| LinkingTo: | Rcpp, RcppEigen (≥ 0.3.3.3.0) |
| RoxygenNote: | 7.1.1 |
| Encoding: | UTF-8 |
| Suggests: | tinytest |
| NeedsCompilation: | yes |
| Packaged: | 2021-06-23 08:36:27 UTC; hhh |
| Author: | Eric Anderson [aut, cre], Alexander Sherikov [cph, ctb] |
| Maintainer: | Eric Anderson <anderic1@gmx.com> |
| Repository: | CRAN |
| Date/Publication: | 2021-06-23 10:00:02 UTC |
Set qpmad parameters
Description
Conveniently set qpmad parameters. Please always use named arguments since parameters can change without notice between releases. In a future version specifying the argument names will be mandatory.
Usage
qpmadParameters(
isFactorized = FALSE,
maxIter = -1,
tol = 1e-12,
checkPD = TRUE,
factorizationType = "NONE",
withLagrMult = FALSE,
returnInvCholFac = FALSE
)
Arguments
isFactorized |
Deprecated, will be removed in a future version. Please use |
maxIter |
Maximum number of iterations, if not positive then no limit. |
tol |
Convergence tolerance. |
checkPD |
Deprecated. Ignored, will be removed in a future release. |
factorizationType |
IF |
withLagrMult |
If |
returnInvCholFac |
If |
Value
a list suitable to be used as the pars-argument to solveqp
See Also
Examples
qpmadParameters(withLagrMult = TRUE)
Quadratic Programming
Description
Solves
argmin 0.5 x' H x + h' x
s.t.
lb_i \leq x_i \leq ub_i
Alb_i \leq (A x)_i \leq Aub_i
Usage
solveqp(
H,
h = NULL,
lb = NULL,
ub = NULL,
A = NULL,
Alb = NULL,
Aub = NULL,
pars = list()
)
Arguments
H |
Symmetric positive definite matrix, n*n. Can also be a (inverse) Cholesky factor cf.
|
h |
Optional, vector of length n. |
lb, ub |
Optional, lower/upper bounds of |
A |
Optional, constraints matrix of dimension p*n, where each row corresponds to a constraint. For equality constraints let corresponding elements in |
Alb, Aub |
Optional, lower/upper bounds for |
pars |
Optional, qpmad-solver parameters, conveniently set with |
Value
At least one of lb, ub or A must be specified. If A has been
specified then also at least one of Alb or Aub. Returns a list with elements solution (the solution vector),
status (a status code) and message (a human readable message). If status = 0 the algorithm has converged.
Possible status codes:
0: Ok-1: Numerical issue, matrix (probably) not positive definite1: Inconsistent2: Infeasible equality3: Infeasible inequality4: Maximal number of iterations
See Also
Examples
## Assume we want to minimize: -(0 5 0) %*% b + 1/2 b^T b
## under the constraints: A^T b >= b0
## with b0 = (-8,2,0)^T
## and (-4 2 0)
## A = (-3 1 -2)
## ( 0 0 1)
## we can use solveqp as follows:
##
Dmat <- diag(3)
dvec <- c(0,-5,0)
Amat <- t(matrix(c(-4,-3,0,2,1,0,0,-2,1),3,3))
bvec <- c(-8,2,0)
solveqp(Dmat,dvec,A=Amat,Alb=bvec)