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qpmadr provides R-bindings to the quadratic
programming-solver qpmad, written by Alexander Sherikov.
You can install the released version of qpmadr from CRAN with:
install.packages("qpmadr")This is an example which shows you how to solve a simple problem:
[ _{}{ x’H x} ]
[ s.t. _{i}{x_i} = n ]
[ -2 x_i ]
where (H) is a random positive definite matrix of size (n n), and (x) is a (column) vector of size (n).
The code below will run a benchmark against the quadprog solver for n=100, checking that both give the same results.
library(qpmadr)
library(quadprog)
library(microbenchmark)
set.seed(42)
n = 100
H = crossprod(matrix(rnorm(n*n), n))
# constraint specification for qpmadr
lb = -2
ub = 2
A = matrix(1, 1, n)
Alb = n
Aub = n
# constraint specification for quadprog
At = cbind(rep_len(1, n), diag(1, n, n), diag(-1, n, n))
b = c(n, rep_len(-2, 2*n))
bm = microbenchmark(
check = "equal",
qpmadr = qpmadr::solveqp(H, lb=lb, ub=ub, A=A, Alb=Alb, Aub=Aub)$solution,
quadprog = quadprog::solve.QP(H, numeric(n), At, b, meq=1)$solution
)
knitr::kable(summary(bm, "relative"), digits=1)| expr | min | lq | mean | median | uq | max | neval |
|---|---|---|---|---|---|---|---|
| qpmadr | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 100 |
| quadprog | 2.7 | 2.5 | 2.5 | 2.8 | 2.4 | 2.5 | 100 |
Timings are relative.
The solver is a c++ header-only library and can be used in other packages via the LinkingTo: field
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.