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# This library is free software; you can redistribute it and/or
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# modify it under the terms of the GNU Library General Public
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# License as published by the Free Software Foundation; either
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# version 2 of the License, or (at your option) any later version.
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# This library is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR Description. See the
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# GNU Library General Public License for more details.
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# You should have received a copy of the GNU Library General
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# Public License along with this library; if not, write to the
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# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
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################################################################################
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# FUNCTION: DESCRIPTION:
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# solveRquadprog Portfolio interface to solver Rquadprog
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# .rquadprogArguments Returns arguments for solver
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# .rquadprog Wrapper to solver function
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# .rquadprogControl Returns default controls for solver
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################################################################################
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function(data, spec, constraints)
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# Portfolio interface to solver Rquadprog
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# solveRquadprog(data, spec, constraints)[-3]
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# solveRquadprog(.lppData, .mvSpec, "LongOnly")[-3]
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# solveRquadprog(.lppData, .mvSpec, "LongOnly")$optim$args
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# solveRquadprog(.lppData, .mvSpec, c("LongOnly", "partial"))$optim$args
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# solveRquadprog(.lppData, .mvSpec, .BoxGroups)[-3]
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# portfolioTest("MV", "minRisk", "solveRquadprog", "LongOnly")
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# portfolioTest("MV", "minRisk", "solveRquadprog", "BoxGroup")
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Data = portfolioData(data, spec)
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nAssets = getNAssets(Data)
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# Solve two Assets Portfolio Analytically:
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ans = .mvSolveTwoAssets(data, spec, constraints)
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# ... this is only for 'unlimited' LongOnly constraints,
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# box and group constraints are discarded here.
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# Compile Arguments for Solver:
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args = .rquadprogArguments(data, spec, constraints)
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# Solve Multiassets Portfolio:
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################################################################################
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.rquadprogArguments <-
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function(data, spec, constraints)
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# Returns quadprog conform arguments for the solver
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# .rquadprogArguments(.lppData, .mvSpec, "LongOnly")
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# .rquadprogArguments(.lppData, .mvSpec, .BoxGroups)
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# Data and Constraints as S4 Objects:
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Data = portfolioData(data, spec)
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Sigma = getSigma(Data)
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nAssets = getNAssets(Data)
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# Set up A_mat of Constraints:
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eqsumW = eqsumWConstraints(data, spec, constraints)
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minsumW = minsumWConstraints(data, spec, constraints)
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maxsumW = maxsumWConstraints(data, spec, constraints)
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Amat = rbind(eqsumW[, -1], diag(nAssets), -diag(nAssets))
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if(!is.null(minsumW)) Amat = rbind(Amat, minsumW[, -1])
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if(!is.null(maxsumW)) Amat = rbind(Amat, -maxsumW[, -1])
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# Set up Vector A_mat >= bvec of Constraints:
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minW = minWConstraints(data, spec, constraints)
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maxW = maxWConstraints(data, spec, constraints)
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bvec = c(eqsumW[, 1], minW, -maxW)
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if(!is.null(minsumW)) bvec = c(bvec, minsumW[, 1])
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if(!is.null(maxsumW)) bvec = c(bvec, -maxsumW[, 1])
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# Part (meq=1) or Full (meq=2) Investment, the Default ?
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rep("==", times = meq),
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rep(">=", times = length(bvec) - meq))
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Dmat = Sigma, dvec = rep(0, nAssets),
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Amat = t(Amat), bvec = bvec, meq = meq, dir = dir)
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################################################################################
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function(Dmat, dvec, Amat, bvec, meq)
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# Goldfarb and Idnani's quadprog solver function
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# Requires to load contributed R package quadprog from which we use
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# the Fortran subroutine of the quadratic solver.
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# Title: Functions to solve Quadratic Programming Problems.
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# Author: S original by Berwin A. Turlach <berwin.turlach@anu.edu.au>
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# R port by Andreas Weingessel <Andreas.Weingessel@ci.tuwien.ac.at>
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# Maintainer: Andreas Weingessel <Andreas.Weingessel@ci.tuwien.ac.at>
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# Description: This package contains routines and documentation for
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# solving quadratic programming problems.
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# Value of slove.QP():
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# solution - vector containing the solution of the quadratic
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# programming problem.
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# value - scalar, the value of the quadratic function at the
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# unconstrained.solution - vector containing the unconstrained
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# minimizer of the quadratic function.
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# iterations - vector of length 2, the first component contains
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# the number of iterations the algorithm needed, the second
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# indicates how often constraints became inactive after
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# becoming active first. vector with the indices of the
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# active constraints at the solution.
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work = rep(0, 2 * n + r * (r + 5)/2 + 2 * q + 1)
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optim = .Fortran("qpgen2",
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dvec = as.double(dvec),
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sol = as.double(rep(0, n)),
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crval = as.double(0),
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iact = as.integer(rep(0, q)),
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nact = as.integer(0),
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iter = as.integer(rep(0, 2)),
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work = as.double(work),
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ierr = as.integer(0),
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PACKAGE = "quadprog")
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# Set Tiny Weights to Zero:
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weights = .checkWeights(optim$sol)
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attr(weights, "invest") = sum(weights)
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# Compose Output List:
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solver = "solveRquadprog",
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targetReturn = bvec[1],
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targetRisk = sqrt(optim$sol %*% Dmat %*% optim$sol)[[1,1]],
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objective = sqrt(optim$sol %*% Dmat %*% optim$sol)[[1,1]],
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################################################################################
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# Returns default quadprog control settings
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# This algorithm comes with no control parameter list
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################################################################################
added
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R/solveRquadprog.R
