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# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR Description. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
################################################################################
# FUNCTION: DESCRIPTION:
# solveRquadprog.CLA Portfolio interface to solver Rquadprog
# .claRquadprogArguments Returns arguments for solver
# FUNCTION: DESCRIPTION:
# .quadprog.CLA Wrapper to solver function
################################################################################
solveRquadprog.CLA <-
function(data, spec, constraints)
{
# A function implemented by Diethelm Wuertz
# Description:
# Portfolio interface to solver Rquadprog
# Example:
# solveRquadprogCLA(data, spec, constraints)[-3]
# FUNCTION:
# Update Specification:
setTargetReturn(spec) <- NA
# Transform Data:
Data <- portfolioData(data, spec)
nAssets <- getNAssets(Data)
# Compile Arguments for Solver:
args <- .claRquadprogArguments(data, spec, constraints)
# Solve Multiassets Portfolio:
ans <- .quadprog.CLA(
Dmat = args$Dmat,
dvec = args$dvec,
Amat = args$Amat,
bvec = args$bvec,
meq = args$meq,
lambda = args$lambda)
# Save Arguments:
ans$optim$args <- args
class(ans) <- c("solver", "list")
# Return Value:
ans
}
################################################################################
.claRquadprogArguments <-
function(data, spec, constraints)
{
# A function implemented by Diethelm Wuertz
# Description:
# Returns quadprog conform arguments for the solver
# FUNCTION:
# Set up the default quadprog QP
ans <- .rquadprogArguments(data, spec, constraints)
# Optimize:
# min(-d^T x + 1/2 x^T D x)
# Start from it and modify for CLA:
lambda <- spec@model$param$lambda
ans$Dmat <- lambda * getSigma(portfolioData(data)) / 2
ans$dvec <- getMu(portfolioData(data))
ans$Amat <- ans$Amat[, -1]
ans$bvec <- ans$bvec[-1]
ans$meq <- ans$meq - 1
ans$dir <- ans$dir[-1]
ans$lambda <- lambda
# Return Value:
ans
}
################################################################################
.quadprog.CLA <-
function(Dmat, dvec, Amat, bvec, meq, lambda)
{
# A function implemented by Diethelm Wuertz
# Description:
# Goldfarb and Idnani's quadprog solver function
# Note:
# Requires to load contributed R package quadprog from which we use
# the Fortran subroutine of the quadratic solver.
# Package: quadprog
# Title: Functions to solve Quadratic Programming Problems.
# Author: S original by Berwin A. Turlach <berwin.turlach@anu.edu.au>
# R port by Andreas Weingessel <Andreas.Weingessel@ci.tuwien.ac.at>
# Maintainer: Andreas Weingessel <Andreas.Weingessel@ci.tuwien.ac.at>
# Description: This package contains routines and documentation for
# solving quadratic programming problems.
# License: GPL-2
# Value of slove.QP():
# solution - vector containing the solution of the quadratic
# programming problem.
# value - scalar, the value of the quadratic function at the
# solution
# unconstrained.solution - vector containing the unconstrained
# minimizer of the quadratic function.
# iterations - vector of length 2, the first component contains
# the number of iterations the algorithm needed, the second
# indicates how often constraints became inactive after
# becoming active first. vector with the indices of the
# active constraints at the solution.
# FUNCION:
# Optimize:
optim <- quadprog::solve.QP(
Dmat = Dmat,
dvec = dvec,
Amat = Amat,
bvec = bvec,
meq = meq,
factorized = FALSE)
# Set Tiny Weights to Zero:
weights <- .checkWeights(optim$solution)
attr(weights, "invest") <- sum(weights)
# Compose Output List:
Sigma <- Dmat * 2 / lambda
mu <- dvec
ans <- list(
type = "MV",
solver = "solveRquadprog.CLA",
optim = optim,
weights = weights,
solution = weights,
targetReturn = (optim$solution %*% mu)[[1,1]],
targetRisk = sqrt(optim$solution %*% Sigma %*% optim$solution)[[1,1]],
objective = optim$crval,
# To do: Add status information
status = 0,
message = "minRisk")
# Return Value:
ans
}
################################################################################
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