`qpmadr` provides R-bindings to the quadratic programming-solver `qpmad`, written by Alexander Sherikov.

## Installation

You can install the released version of qpmadr from CRAN with:

``install.packages("qpmadr")``

## Example

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 2 ]

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(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.

## C++-interface

The solver is a c++ header-only library and can be used in other packages via the LinkingTo: field