solve.QP {quadprog} | R Documentation |
This routine implements the dual method of Goldfarb and Idnani (1982, 1983) for solving quadratic programming problems of the form min(-d^T b + 1/2 b^T D b) with the constraints A^T b >= b_0.
solve.QP(Dmat, dvec, Amat, bvec, meq=0, factorized=FALSE)
a list with the following components:
solution |
vector containing the solution of the quadratic programming problem. |
value |
scalar, the value of the quadratic function at the solution |
unconstrained.solution |
vector containing the unconstrained minimizer of the quadratic function. |
iterations |
vector of length 2, the first component contains the number of iterations the algorithm needed, the second indicates how often constraints became inactive after becoming active first. vector with the indices of the active constraints at the solution. |
Goldfarb, D. and Idnani, A. (1982). Dual and Primal-Dual Methods for Solving Strictly Convex Quadratic Programs. In Numerical Analysis J.P. Hennart, ed. Springer-Verlag, Berlin. pp. 226-239.
Goldfarb, D. and Idnani, A. (1983). A numerically stable dual method for solving strictly convex quadratic programs. Mathematical Programming 27, 1-33.
# # 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 solve.QP as follows: # Dmat <- matrix(0,3,3) diag(Dmat) <- 1 dvec <- c(0,5,0) Amat <- matrix(c(-4,-3,0,2,1,0,0,-2,1),3,3) bvec <- c(-8,2,0) solve.QP(Dmat,dvec,Amat,bvec=bvec)