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solnp.R
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solnp.R
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#################################################################################
##
## R package Rsolnp by Alexios Ghalanos and Stefan Theussl Copyright (C) 2009-2013
## This file is part of the R package Rsolnp.
##
## The R package Rsolnp is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## The R package Rsolnp 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 PURPOSE. See the
## GNU General Public License for more details.
##
#################################################################################
# Based on the original solnp by Yinyu Ye
# http://www.stanford.edu/~yyye/Col.html
#----------------------------------------------------------------------------------
# The Function SOLNP solves nonlinear programs in standard form:
#
# minimize J(P)
# subject to EC(P) =0
# IB(:,1)<= IC(P) <=IB(:,2)
# PB(:,1)<= P <=PB(:,2).
#where
#
# J : Cost objective scalar function
# EC : Equality constraint vector function
# IC : Inequality constraint vector function
# P : Decision parameter vector
# IB, PB : lower and upper bounds for IC and P.
#----------------------------------------------------------------------------------
# control list
# RHO : penalty parameter
# MAJIT: maximum number of major iterations
# MINIT: maximum number of minor iterations
# DELTA: relative step size in forward difference evaluation
# TOL : tolerance on feasibility and optimality
# defaults RHO=1, MAJIT=10, MINIT=10, DELTA=1.0e-5, TOL=1.0e-4
solnp = function(pars, fun, eqfun = NULL, eqB = NULL, ineqfun = NULL, ineqLB = NULL, ineqUB = NULL, LB = NULL, UB = NULL, control = list(), ...)
{
# start timer
tic = Sys.time()
xnames = names(pars)
# get environment
.solnpenv <- environment()
assign("xnames", xnames, envir = .solnpenv)
# initiate function count
assign(".solnp_nfn", 0, envir = .solnpenv)
assign(".solnp_errors", 0, envir = .solnpenv)
# index of function indicators
# [1] length of pars
# [2] has function gradient?
# [3] has hessian?
# [4] has ineq?
# [5] ineq length
# [6] has jacobian (inequality)
# [7] has eq?
# [8] eq length
# [9] has jacobian (equality)
# [10] has upper / lower bounds
# [11] has either lower/upper bounds or ineq
ind = rep(0, 11)
np = ind[1] = length(pars)
# lower parameter bounds - indicator
# lpb[1]=1 means lower/upper bounds present
# lpb[2]=1 means lower/upper bounds OR inequality bounds present
# do parameter and LB/UB checks
check1 = .checkpars(pars, LB, UB, .solnpenv)
# .LB and .UB assigned
.LB = get(".LB", envir = .solnpenv)
.UB = get(".UB", envir = .solnpenv)
if( !is.null(.LB) || !is.null(.UB) ) ind[10] = 1
# do function checks and return starting value
funv = .checkfun(pars, fun, .solnpenv, ...)
#.solnp_fun assigned
.solnp_fun = get(".solnp_fun", envir = .solnpenv)
# Analytical Gradient Functionality not yet implemented in subnp function
# gradient and hessian checks
#if(!is.null(grad)){
# gradv = .checkgrad(pars, grad, .solnpenv, ...)
# ind[2] = 1
#} else{
# .solnp_gradfun = function(pars, ...) .fdgrad(pars, fun = .solnp_fun, ...)
ind[2] = 0
# gradv = .solnp_gradfun(pars, ...)
#}
# .solnp_gradfun(pars, ...) assigned
.solnp_hessfun = NULL
ind[3] = 0
#hessv = NULL
# .solnp_hessfun(pars, ...) assigned
# do inequality checks and return starting values
if(!is.null(ineqfun)){
ineqv = .checkineq(pars, ineqfun, ineqLB, ineqUB, .solnpenv, ...)
ind[4] = 1
nineq = length(ineqLB)
ind[5] = nineq
# check for infinities/nans
.ineqLBx = .ineqLB
.ineqUBx = .ineqUB
.ineqLBx[!is.finite(.ineqLB)] = -1e10
.ineqUBx[!is.finite(.ineqUB)] = 1e10
ineqx0 = (.ineqLBx + .ineqUBx)/2
#if(!is.null(ineqgrad)){
# ineqjacv = .cheqjacineq(pars, gradineq, .ineqUB, .ineqLB, .solnpenv, ...)
# ind[6] = 1
#} else{
# .solnp_ineqjac = function(pars, ...) .fdjac(pars, fun = .solnp_ineqfun, ...)
ind[6] = 0
#ineqjacv = .solnp_ineqjac(pars, ...)
#}
} else{
.solnp_ineqfun = function(pars, ...) .emptyfun(pars, ...)
# .solnp_ineqjac = function(pars, ...) .emptyjac(pars, ...)
ineqv = NULL
ind[4] = 0
nineq = 0
ind[5] = 0
ind[6] = 0
ineqx0 = NULL
.ineqLB = NULL
.ineqUB = NULL
}
# .solnp_ineqfun and .solnp_ineqjac assigned
# .ineqLB and .ineqUB assigned
.solnp_ineqfun = get(".solnp_ineqfun", envir = .solnpenv)
.ineqLB = get(".ineqLB", envir = .solnpenv)
.ineqUB = get(".ineqUB", envir = .solnpenv)
# equality checks
if(!is.null(eqfun)){
eqv = .checkeq(pars, eqfun, eqB, .solnpenv, ...)
ind[7] = 1
.eqB = get(".eqB", envir = .solnpenv)
neq = length(.eqB)
ind[8] = neq
#if(!is.null(eqgrad)){
# eqjacv = .cheqjaceq(pars, gradeq, .solnpenv, ...)
# ind[9] = 1
#} else{
# .solnp_eqjac = function(pars, ...) .fdjac(pars, fun = .solnp_eqfun, ...)
# eqjacv = .solnp_eqjac(pars, ...)
ind[9] = 0
#}
} else {
eqv = NULL
#eqjacv = NULL
.solnp_eqfun = function(pars, ...) .emptyfun(pars, ...)
#.solnp_eqjac = function(pars, ...) .emptyjac(pars, ...)
ind[7] = 0
neq = 0
ind[8] = 0
ind[9] = 0
}
# .solnp_eqfun(pars, ...) and .solnp_eqjac(pars, ...) assigned
# .solnp_eqB assigned
.solnp_eqfun = get(".solnp_eqfun", envir = .solnpenv)
if( ind[ 10 ] || ind [ 4 ]) ind[ 11 ] = 1
# parameter bounds (pb)
pb = rbind( cbind(.ineqLB, .ineqUB), cbind(.LB, .UB) )
# check control list
ctrl = .solnpctrl( control )
rho = ctrl[[ 1 ]]
# maxit = outer iterations
maxit = ctrl[[ 2 ]]
# minit = inner iterations
minit = ctrl[[ 3 ]]
delta = ctrl[[ 4 ]]
tol = ctrl[[ 5 ]]
trace = ctrl[[ 6 ]]
# total constraints (tc) = no.inequality constraints + no.equality constraints
tc = nineq + neq
# initialize fn value and inequalities and set to NULL those not needed
j = jh = funv
tt = 0 * .ones(3, 1)
if( tc > 0 ) {
# lagrange multipliers (lambda)
lambda = 0 * .ones(tc, 1)
# constraint vector = [1:neq 1:nineq]
constraint = c(eqv, ineqv)
if( ind[4] ) {
tmpv = cbind(constraint[ (neq + 1):tc ] - .ineqLB, .ineqUB - constraint[ (neq + 1):tc ] )
testmin = apply( tmpv, 1, FUN = function( x ) min(x[ 1 ], x[ 2 ]) )
if( all(testmin > 0) ) ineqx0 = constraint[ (neq + 1):tc ]
constraint[ (neq + 1):tc ] = constraint[ (neq + 1):tc ] - ineqx0
}
tt[ 2 ] = .vnorm(constraint)
if( max(tt[ 2 ] - 10 * tol, nineq, na.rm = TRUE) <= 0 ) rho = 0
} else{
lambda = 0
}
# starting augmented parameter vector
p = c(ineqx0, pars)
hessv = diag(np + nineq)
mu = np
.solnp_iter = 0
ob = c(funv, eqv, ineqv)
while( .solnp_iter < maxit ){
.solnp_iter = .solnp_iter + 1
.subnp_ctrl = c(rho, minit, delta, tol, trace)
# make the scale for the cost, the equality constraints, the inequality
# constraints, and the parameters
if( ind[7] ) {
# [1 neq]
vscale = c( ob[ 1 ], rep(1, neq) * max( abs(ob[ 2:(neq + 1) ]) ) )
} else {
vscale = 1
}
if( !ind[ 11 ] ) {
vscale = c(vscale, p)
} else {
# [ 1 neq np]
vscale = c(vscale, rep( 1, length.out = length(p) ) )
}
vscale = apply( matrix(vscale, ncol = 1), 1, FUN = function( x ) min( max( abs(x), tol ), 1/tol ) )
res = .subnp(pars = p, yy = lambda, ob = ob, hessv = hessv, lambda = mu, vscale = vscale,
ctrl = .subnp_ctrl, .env = .solnpenv, ...)
if(get(".solnp_errors", envir = .solnpenv) == 1){
maxit = .solnp_iter
}
p = res$p
lambda = res$y
hessv = res$hessv
mu = res$lambda
temp = p[ (nineq + 1):(nineq + np) ]
funv = .safefunx(temp, .solnp_fun, .env = .solnpenv, ...)
ctmp = get(".solnp_nfn", envir = .solnpenv)
assign(".solnp_nfn", ctmp + 1, envir = .solnpenv)
tempdf = cbind(temp, funv)
if( trace ){
.report(.solnp_iter, funv, temp)
}
eqv = .solnp_eqfun(temp, ...)
ineqv = .solnp_ineqfun(temp, ...)
ob = c(funv, eqv, ineqv)
tt[ 1 ] = (j - ob[ 1 ]) / max(abs(ob[ 1 ]), 1)
j = ob[ 1 ]
if( tc > 0 ){
constraint = ob[ 2:(tc + 1) ]
if( ind[ 4 ] ){
tempv = rbind( constraint[ (neq + 1):tc ] - pb[ 1:nineq, 1 ],
pb[ 1:nineq, 2 ] - constraint[ (neq + 1):tc ] )
if( min(tempv) > 0 ) {
p[ 1:nineq ] = constraint[ (neq + 1):tc ]
}
constraint[ (neq + 1):tc ] = constraint[ (neq + 1):tc ] - p[ 1:nineq ]
}
tt[ 3 ] = .vnorm(constraint)
if( tt[ 3 ] < 10 * tol ) {
rho = 0
mu = min(mu, tol)
}
if( tt[ 3 ] < 5 * tt[ 2 ]) {
rho = rho/5
}
if( tt[ 3 ] > 10 * tt[ 2 ]) {
rho = 5 * max( rho, sqrt(tol) )
}
if( max( c( tol + tt[ 1 ], tt[ 2 ] - tt[ 3 ] ) ) <= 0 ) {
lambda = 0
hessv = diag( diag ( hessv ) )
}
tt[ 2 ] = tt[ 3 ]
}
if( .vnorm( c(tt[ 1 ], tt[ 2 ]) ) <= tol ) {
maxit = .solnp_iter
}
jh = c(jh, j)
}
if( ind[ 4 ] ) {
ineqx0 = p[ 1:nineq ]
}
p = p[ (nineq + 1):(nineq + np) ]
if(get(".solnp_errors", envir = .solnpenv) == 1){
convergence = 2
if( trace ) cat( paste( "\nsolnp--> Solution not reliable....Problem Inverting Hessian.\n", sep="" ) )
} else{
if( .vnorm( c(tt[ 1 ], tt[ 2 ]) ) <= tol ) {
convergence = 0
# if( trace ) cat( paste( "\nsolnp--> Completed in ", .solnp_iter, " iterations\n", sep="" ) )
} else{
convergence = 1
# if( trace ) cat( paste( "\nsolnp--> Exiting after maximum number of iterations\n",
# "Tolerance not achieved\n", sep="" ) )
}
}
# end timer
ctmp = get(".solnp_nfn", envir = .solnpenv)
toc = Sys.time() - tic
names(p) = xnames
ans = list(pars = p, convergence = convergence, values = as.numeric(jh), lagrange = lambda,
hessian = hessv, ineqx0 = ineqx0, nfuneval = ctmp, outer.iter = .solnp_iter,
elapsed = toc, vscale = vscale)
return( ans )
}
#################################################################################
##
## R package Rsolnp by Alexios Ghalanos and Stefan Theussl Copyright (C) 2009-2013
## This file is part of the R package Rsolnp.
##
## The R package Rsolnp is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## The R package Rsolnp 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 PURPOSE. See the
## GNU General Public License for more details.
##
#################################################################################
# Based on the original subnp Yinyu Ye
# http://www.stanford.edu/~yyye/Col.html
.subnp = function(pars, yy, ob, hessv, lambda, vscale, ctrl, .env, ...)
{
.solnp_fun = get(".solnp_fun", envir = .env)
.solnp_eqfun = get(".solnp_eqfun", envir = .env)
.solnp_ineqfun = get(".solnp_ineqfun", envir = .env)
ineqLB = get(".ineqLB", envir = .env)
ineqUB = get(".ineqUB", envir = .env)
LB = get(".LB", envir = .env)
UB = get(".UB", envir = .env)
#.solnp_gradfun = get(".solnp_gradfun", envir = .env)
#.solnp_eqjac = get(".solnp_eqjac", envir = .env)
#.solnp_ineqjac = get(".solnp_ineqjac", envir = .env)
ind = get("ind", envir = .env)
# pars [nineq + np]
rho = ctrl[ 1 ]
maxit = ctrl[ 2 ]
delta = ctrl[ 3 ]
tol = ctrl[ 4 ]
trace = ctrl[ 5 ]
# [1] length of pars
# [2] has function gradient?
# [3] has hessian?
# [4] has ineq?
# [5] ineq length
# [6] has jacobian (inequality)
# [7] has eq?
# [8] eq length
# [9] has jacobian (equality)
# [10] has upper / lower bounds
# [11] has either lower/upper bounds or ineq
neq = ind[ 8 ]
nineq = ind[ 5 ]
np = ind[ 1 ]
ch = 1
alp = c(0,0,0)
nc = neq + nineq
npic = np + nineq
p0 = pars
# pb [ 2 x (nineq + np) ]
pb = rbind( cbind(ineqLB, ineqUB), cbind(LB,UB) )
sob = numeric()
ptt = matrix()
sc = numeric()
# scale the cost, the equality constraints, the inequality constraints,
# the parameters (inequality parameters AND actual parameters),
# and the parameter bounds if there are any
# Also make sure the parameters are no larger than (1-tol) times their bounds
# ob [ 1 neq nineq]
ob = ob / vscale[ 1:(nc + 1) ]
# p0 [np]
p0 = p0 / vscale[ (neq + 2):(nc + np + 1) ]
if( ind[ 11 ] ) {
if( !ind[ 10 ] ) {
mm = nineq
} else {
mm = npic
}
pb = pb / cbind(vscale[ (neq + 2):(neq + mm + 1) ], vscale[ (neq + 2):(neq + mm + 1) ])
}
# scale the lagrange multipliers and the Hessian
if( nc > 0) {
# yy [total constraints = nineq + neq]
# scale here is [tc] and dot multiplied by yy
yy = vscale[ 2:(nc + 1) ] * yy / vscale[ 1 ]
}
# yy = [zeros 3x1]
# h is [ (np+nineq) x (np+nineq) ]
#columnvector %*% row vector (size h) then dotproduct h then dotdivide scale[1]
hessv = hessv * (vscale[ (neq + 2):(nc + np + 1) ] %*% t(vscale[ (neq + 2):(nc + np + 1)]) ) / vscale[ 1 ]
# h[ 8x8 eye]
j = ob[ 1 ]
if( ind[4] ) {
if( !ind[7] ) {
# [nineq x (nineq+np) ]
a = cbind( -diag(nineq), matrix(0, ncol = np, nrow = nineq) )
} else {
# [ (neq+nineq) x (nineq+np)]
a = rbind( cbind( 0 * .ones(neq, nineq), matrix(0, ncol = np, nrow = neq) ),
cbind( -diag(nineq), matrix(0, ncol = np, nrow = nineq) ) )
}
}
if( ind[7] && !ind[4] ) {
a = .zeros(neq, np)
}
if( !ind[7] && !ind[4] ) {
a = .zeros(1, np)
}
# gradient
g= 0 * .ones(npic, 1)
p = p0 [ 1:npic ]
if( nc > 0 ) {
# [ nc ]
constraint = ob[ 2:(nc + 1) ]
# constraint [5 0 11 3x1]
# gradient routine
for( i in 1:np ) {
# scale the parameters (non ineq)
p0[ nineq + i ] = p0[ nineq + i ] + delta
tmpv = p0[ (nineq + 1):npic ] * vscale[ (nc + 2):(nc + np + 1) ]
funv = .safefunx(tmpv, .solnp_fun, .env, ...)
eqv = .solnp_eqfun(tmpv, ...)
ineqv = .solnp_ineqfun(tmpv, ...)
ctmp = get(".solnp_nfn", envir = .env)
assign(".solnp_nfn", ctmp + 1, envir = .env)
ob = c(funv, eqv, ineqv) / vscale[ 1:(nc + 1) ]
g[ nineq + i ] = (ob[ 1 ] - j) / delta
a[ , nineq + i ] = (ob[ 2:(nc + 1) ] - constraint) / delta
p0[ nineq + i ] = p0[ nineq + i ] - delta
}
if( ind[4] ) {
constraint[ (neq + 1):(neq + nineq) ] = constraint[ (neq + 1):(neq + nineq) ] - p0[ 1:nineq ]
}
# solver messages
if( .solvecond(a) > 1 / .eps ) {
if( trace ) .subnpmsg( "m1" )
}
# a(matrix) x columnvector - columnvector
# b [nc,1]
b = a %*% p0 - constraint
ch = -1
alp[ 1 ] = tol - max( abs(constraint) )
if( alp[ 1 ] <= 0 ) {
ch = 1
if( !ind[11] ) {
# a %*% t(a) gives [nc x nc]
# t(a) %*% above gives [(np+nc) x 1]
p0 = p0 - t(a) %*% solve(a %*% t(a), constraint)
alp[ 1 ] = 1
}
}
if( alp[ 1 ] <= 0 ) {
# this expands p0 to [nc+np+1]
p0[ npic + 1 ] = 1
a = cbind(a, -constraint)
# cx is rowvector
cx = cbind(.zeros(1,npic), 1)
dx = .ones(npic + 1, 1)
go = 1
minit = 0
while( go >= tol ) {
minit = minit + 1
# gap [(nc + np) x 2]
gap = cbind(p0[ 1:mm ] - pb[ , 1 ], pb[ , 2 ] - p0[ 1:mm ] )
# this sorts every row
gap = t( apply( gap, 1, FUN=function( x ) sort(x) ) )
dx[ 1:mm ] = gap[ , 1 ]
# expand dx by 1
dx[ npic + 1 ] = p0[ npic + 1 ]
if( !ind[10] ) {
dx[ (mm + 1):npic ] = max( c(dx[ 1:mm ], 100) ) * .ones(npic - mm, 1)
}
# t( a %*% diag( as.numeric(dx) ) ) gives [(np+nc + 1 (or more) x nc]
# dx * t(cx) dot product of columnvectors
# qr.solve returns [nc x 1]
# TODO: Catch errors here
y = try( qr.solve( t( a %*% diag( as.numeric(dx) , length(dx), length(dx) ) ), dx * t(cx) ), silent = TRUE)
if(inherits(y, "try-error")){
p = p0 * vscale[ (neq + 2):(nc + np + 1) ] # unscale the parameter vector
if( nc > 0 ) {
y = 0 # unscale the lagrange multipliers
}
hessv = vscale[ 1 ] * hessv / (vscale[ (neq + 2):(nc + np + 1) ] %*% t(vscale[ (neq + 2):(nc + np + 1) ]) )
ans = list(p = p, y = y, hessv = hessv, lambda = lambda)
assign(".solnp_errors", 1, envir = .env)
return(ans)
}
v = dx * ( dx *(t(cx) - t(a) %*% y) )
if( v[ npic + 1 ] > 0 ) {
z = p0[ npic + 1 ] / v[ npic + 1 ]
for( i in 1:mm ) {
if( v[ i ] < 0 ) {
z = min(z, -(pb[ i, 2 ] - p0[ i ]) / v[ i ])
} else if( v[ i ] > 0 ) {
z = min( z, (p0[ i ] - pb[ i , 1 ]) / v[ i ])
}
}
if( z >= p0[ npic + 1 ] / v[ npic + 1 ] ) {
p0 = p0 - z * v
} else {
p0 = p0 - 0.9 * z * v
}
go = p0[ npic + 1 ]
if( minit >= 10 ) {
go = 0
}
} else {
go = 0
minit = 10
}
}
if( minit >= 10 ) {
if( trace ) .subnpmsg( "m2" )
}
a = matrix(a[ , 1:npic ], ncol = npic)
b = a %*% p0[ 1:npic ]
}
}
p = p0 [ 1:npic ]
y = 0
if( ch > 0 ) {
tmpv = p[ (nineq + 1):npic ] * vscale[ (nc + 2):(nc + np + 1) ]
funv = .safefunx(tmpv, .solnp_fun, .env,...)
eqv = .solnp_eqfun(tmpv, ...)
ineqv = .solnp_ineqfun(tmpv, ...)
ctmp = get(".solnp_nfn", envir = .env)
assign(".solnp_nfn", ctmp + 1, envir = .env)
ob = c(funv, eqv, ineqv) / vscale[ 1:(nc + 1) ]
}
j = ob[ 1 ]
if( ind[4] ) {
ob[ (neq + 2):(nc + 1) ] = ob[ (neq + 2):(nc + 1) ] - p[ 1:nineq ]
}
if( nc > 0 ) {
ob[ 2:(nc + 1) ] = ob[ 2:(nc + 1) ] - a %*% p + b
j = ob[ 1 ] - t(yy) %*% matrix(ob[ 2:(nc + 1) ], ncol=1) + rho * .vnorm(ob[ 2:(nc + 1) ]) ^ 2
}
minit = 0
while( minit < maxit ) {
minit = minit + 1
if( ch > 0 ) {
for( i in 1:np ) {
p[ nineq + i ] = p[ nineq + i ] + delta
tmpv = p[ (nineq + 1):npic ] * vscale[ (nc + 2):(nc + np + 1) ]
funv = .safefunx(tmpv, .solnp_fun, .env, ...)
eqv = .solnp_eqfun(tmpv, ...)
ineqv = .solnp_ineqfun(tmpv, ...)
ctmp = get(".solnp_nfn", envir = .env)
assign(".solnp_nfn", ctmp + 1, envir = .env)
obm = c(funv, eqv, ineqv) / vscale[ 1:(nc + 1) ]
if( ind[4] ) {
obm[ (neq + 2):(nc + 1)] = obm[ (neq + 2):(nc + 1) ] - p[ 1:nineq ]
}
if( nc > 0 ) {
obm[ 2:(nc + 1) ] = obm[ 2:(nc + 1) ] - a %*% p + b
obm = obm[ 1 ] - t(yy) %*% obm[ 2:(nc + 1) ] + rho * .vnorm(obm[ 2:(nc + 1 ) ]) ^ 2
}
g[ nineq + i ] = (obm - j) / delta
p[ nineq + i ] = p[ nineq + i ] - delta
}
if( ind[4] ) {
g[ 1:nineq ] = 0
}
}
if( minit > 1 ) {
yg = g - yg
sx = p - sx
sc[ 1 ] = t(sx) %*% hessv %*% sx
sc[ 2 ] = t(sx) %*% yg
if( (sc[ 1 ] * sc[ 2 ]) > 0 ) {
sx = hessv %*% sx
hessv = hessv - ( sx %*% t(sx) ) / sc[ 1 ] + ( yg %*% t(yg) ) / sc[ 2 ]
}
}
dx = 0.01 * .ones(npic, 1)
if( ind[11] ) {
gap = cbind(p[ 1:mm ] - pb[ , 1 ], pb[ , 2 ] - p[ 1:mm ])
gap = t( apply( gap, 1, FUN = function( x ) sort(x) ) )
gap = gap[ , 1 ] + sqrt(.eps) * .ones(mm, 1)
dx[ 1:mm, 1 ] = .ones(mm, 1) / gap
if( !ind[10] ){
dx[ (mm + 1):npic, 1 ] = min (c( dx[ 1:mm, 1 ], 0.01) ) * .ones(npic - mm, 1)
}
}
go = -1
lambda = lambda / 10
while( go <= 0 ) {
cz = try(chol( hessv + lambda * diag( as.numeric(dx * dx), length(dx), length(dx) ) ), silent = TRUE)
if(inherits(cz, "try-error")){
p = p * vscale[ (neq + 2):(nc + np + 1) ] # unscale the parameter vector
if( nc > 0 ) {
y = 0 # unscale the lagrange multipliers
}
hessv = vscale[ 1 ] * hessv / (vscale[ (neq + 2):(nc + np + 1) ] %*% t(vscale[ (neq + 2):(nc + np + 1) ]) )
ans = list(p = p, y = y, hessv = hessv, lambda = lambda)
assign(".solnp_errors", 1, envir = .env)
return(ans)
}
cz = try(solve(cz), silent = TRUE)
if(inherits(cz, "try-error")){
p = p * vscale[ (neq + 2):(nc + np + 1) ] # unscale the parameter vector
if( nc > 0 ) {
y = 0 # unscale the lagrange multipliers
}
hessv = vscale[ 1 ] * hessv / (vscale[ (neq + 2):(nc + np + 1) ] %*% t(vscale[ (neq + 2):(nc + np + 1) ]) )
ans = list(p = p, y = y, hessv = hessv, lambda = lambda)
assign(".solnp_errors", 1, envir = .env)
return(ans)
}
yg = t(cz) %*% g
if( nc == 0 ) {
u = -cz %*% yg
} else{
y = try( qr.solve(t(cz) %*% t(a), yg), silent = TRUE )
if(inherits(y, "try-error")){
p = p * vscale[ (neq + 2):(nc + np + 1) ] # unscale the parameter vector
if( nc > 0 ) {
# y = vscale[ 1 ] * y / vscale[ 2:(nc + 1) ] # unscale the lagrange multipliers
y = 0
}
hessv = vscale[ 1 ] * hessv / (vscale[ (neq + 2):(nc + np + 1) ] %*% t(vscale[ (neq + 2):(nc + np + 1) ]) )
ans = list(p = p, y = y, hessv = hessv, lambda = lambda)
assign(".solnp_errors", 1, envir = .env)
return(ans)
}
u = -cz %*% (yg - ( t(cz) %*% t(a) ) %*% y)
}
p0 = u[ 1:npic ] + p
if( !ind[ 11 ] ) {
go = 1
} else {
go = min( c(p0[ 1:mm ] - pb[ , 1 ], pb[ , 2 ] - p0[ 1:mm ]) )
lambda = 3 * lambda
}
}
alp[ 1 ] = 0
ob1 = ob
ob2 = ob1
sob[ 1 ] = j
sob[ 2 ] = j
ptt = cbind(p, p)
alp[ 3 ] = 1.0
ptt = cbind(ptt, p0)
tmpv = ptt[ (nineq + 1):npic, 3 ] * vscale[ (nc + 2):(nc + np + 1) ]
funv = .safefunx(tmpv, .solnp_fun, .env, ...)
eqv = .solnp_eqfun(tmpv, ...)
ineqv = .solnp_ineqfun(tmpv, ...)
ctmp = get(".solnp_nfn", envir = .env)
assign(".solnp_nfn", ctmp + 1, envir = .env)
ob3 = c(funv, eqv, ineqv) / vscale[ 1:(nc + 1) ]
sob[ 3 ] = ob3[ 1 ]
if( ind[4] ) {
ob3[ (neq + 2):(nc + 1) ] = ob3[ (neq + 2):(nc + 1) ] - ptt[ 1:nineq, 3 ]
}
if( nc > 0 ) {
ob3[ 2:(nc + 1) ] = ob3[ 2:(nc + 1) ] - a %*% ptt[ , 3 ] + b
sob[ 3 ] = ob3[ 1 ] - t(yy) %*% ob3[ 2:(nc + 1) ] + rho * .vnorm(ob3[ 2:(nc + 1) ]) ^ 2
}
go = 1
while( go > tol ) {
alp[ 2 ] = (alp[ 1 ] + alp[ 3 ]) / 2
ptt[ , 2 ] = (1 - alp[ 2 ]) * p + alp[ 2 ] * p0
tmpv = ptt[ (nineq + 1):npic, 2 ] * vscale[ (nc + 2):(nc + np + 1) ]
funv = .safefunx(tmpv, .solnp_fun, .env, ...)
eqv = .solnp_eqfun(tmpv, ...)
ineqv = .solnp_ineqfun(tmpv, ...)
ctmp = get(".solnp_nfn", envir = .env)
assign(".solnp_nfn", ctmp + 1, envir = .env)
ob2 = c(funv, eqv, ineqv) / vscale[ 1:(nc + 1) ]
sob[ 2 ] = ob2[ 1 ]
if( ind[4] ) {
ob2[ (neq + 2):(nc + 1) ] = ob2[ (neq + 2):(nc + 1) ] - ptt[ 1:nineq , 2 ]
}
if( nc > 0 ) {
ob2[ 2:(nc + 1) ] = ob2[ 2:(nc + 1) ] - a %*% ptt[ , 2 ] + b
sob[ 2 ] = ob2[ 1 ] - t(yy) %*% ob2[ 2:(nc + 1) ] + rho * .vnorm(ob2[ 2:(nc + 1) ]) ^ 2
}
obm = max(sob)
if( obm < j ) {
obn = min(sob)
go = tol * (obm - obn) / (j - obm)
}
condif1 = sob[ 2 ] >= sob[ 1 ]
condif2 = sob[ 1 ] <= sob[ 3 ] && sob[ 2 ] < sob[ 1 ]
condif3 = sob[ 2 ] < sob[ 1 ] && sob[ 1 ] > sob[ 3 ]
if( condif1 ) {
sob[ 3 ] = sob[ 2 ]
ob3 = ob2
alp[ 3 ] = alp[ 2 ]
ptt[ , 3 ] = ptt[ , 2 ]
}
if( condif2 ) {
sob[ 3 ] = sob[ 2 ]
ob3 = ob2
alp[ 3 ] = alp[ 2 ]
ptt[ , 3 ] = ptt[ , 2 ]
}
if( condif3 ) {
sob[ 1 ] = sob[ 2 ]
ob1 = ob2
alp[ 1 ] = alp[ 2 ]
ptt[ , 1 ] = ptt[ , 2 ]
}
if( go >= tol ) {
go = alp[ 3 ] - alp[ 1 ]
}
}
sx = p
yg = g
ch = 1
obn = min(sob)
if( j <= obn ) {
maxit = minit
}
reduce = (j - obn) / ( 1 + abs(j) )
if( reduce < tol ) {
maxit = minit
}
condif1 = sob[ 1 ] < sob[ 2 ]
condif2 = sob[ 3 ] < sob[ 2 ] && sob[ 1 ] >= sob[ 2 ]
condif3 = sob[ 1 ] >= sob[ 2 ] && sob[ 3 ] >= sob[ 2 ]
if( condif1 ) {
j = sob[ 1 ]
p = ptt[ , 1 ]
ob = ob1
}
if( condif2 ) {
j = sob [ 3 ]
p = ptt[ , 3 ]
ob = ob3
}
if( condif3 ) {
j = sob[ 2 ]
p = ptt[ , 2 ]
ob = ob2
}
}
p = p * vscale[ (neq + 2):(nc + np + 1) ] # unscale the parameter vector
if( nc > 0 ) {
y = vscale[ 1 ] * y / vscale[ 2:(nc + 1) ] # unscale the lagrange multipliers
}
hessv = vscale[ 1 ] * hessv / (vscale[ (neq + 2):(nc + np + 1) ] %*% t(vscale[ (neq + 2):(nc + np + 1) ]) )
if( reduce > tol ) {
if( trace ) .subnpmsg( "m3" )
}
ans = list(p = p, y = y, hessv = hessv, lambda = lambda)
return( ans )
}
.fdgrad = function(i, p, delta, np, vscale, constraint, j, nineq, npic, nc, .solnp_fun,
.solnp_eqfun, .solnp_ineqfun, .env, ...)
{
ans = list()
px = p
px[ nineq + i ] = px[ nineq + i ] + delta
tmpv = px[ (nineq + 1):npic ] * vscale[ (nc + 2):(nc + np + 1) ]
funv = .safefunx(tmpv, .solnp_fun, .env, ...)
eqv = .solnp_eqfun(tmpv, ...)
ineqv = .solnp_ineqfun(tmpv, ...)
ctmp = get(".solnp_nfn", envir = .env)
assign(".solnp_nfn", ctmp + 1, envir = .env)
ob = c(funv, eqv, ineqv) / vscale[ 1:(nc + 1) ]
ans$p = px
ans$ob = ob
ans$g = (ob[ 1 ] - j) / delta
ans$a = (ob[ 2:(nc + 1) ] - constraint) / delta
return( ans )
}
#################################################################################
##
## R package Rsolnp by Alexios Ghalanos and Stefan Theussl Copyright (C) 2009-2013
## This file is part of the R package Rsolnp.
##
## The R package Rsolnp is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## The R package Rsolnp 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 PURPOSE. See the
## GNU General Public License for more details.
##
#################################################################################
.eps = .Machine$double.eps
.subnpmsg = function(m){
g1 = c("solnp-->")
m1 = paste("\n",g1, "Redundant constraints were found. Poor\n",
g1, "intermediate results may result.Suggest that you\n",
g1, "remove redundant constraints and re-OPTIMIZE\n", sep = "")
m2 = paste("\n",g1, "The linearized problem has no feasible\n",
g1, "solution. The problem may not be feasible.\n", sep = "")
m3 = paste("\n",g1, "Minor optimization routine did not converge in the \n",
g1, "specified number of minor iterations. You may need\n",
g1, "to increase the number of minor iterations. \n", sep = "")
ans = switch(m,
m1 = m1,
m2 = m2,
m3 = m3)
cat(ans)
}
.checkpars = function(pars, LB, UB, .env)
{
if(is.null(pars))
stop("\nsolnp-->error: must supply starting parameters\n", call. = FALSE)
if(!is.null(LB)){
if(length(pars) != length(LB))
stop("\nsolnp-->error: LB length not equal to parameter length\n", call. = FALSE)
if(is.null(UB)) UB = rep(.Machine$double.xmax/2, length(LB))
} else{
LB = NULL
}
if(!is.null(UB)){
if(length(pars) != length(UB))
stop("\nsolnp-->error: UB length not equal to parameter length\n", call. = FALSE)
if(is.null(LB)) LB = rep(-.Machine$double.xmax/2, length(UB))
} else{
UB = NULL
}
if(!is.null(UB) && any(LB > UB))
stop("\nsolnp-->error: UB must be greater than LB\n", call. = FALSE)
if(!is.null(UB) && any(LB == UB))
warning("\nsolnp-->warning: Equal Lower/Upper Bounds Found. Consider\n
excluding fixed parameters.\n", call. = FALSE)
# deal with infinite values as these are not accepted by solve.QP
if(!is.null(LB) && !any(is.finite(LB))){
idx = which(!is.finite(LB))
LB[idx] = sign(LB[idx])*.Machine$double.xmax/2
}
if(!is.null(UB) && !any(is.finite(UB))){
idx = which(!is.finite(UB))
UB[idx] = sign(UB[idx])*.Machine$double.xmax/2
}
assign(".LB", LB, envir = .env)
assign(".UB", UB, envir = .env)
return(1)
}
.checkfun = function(pars, fun, .env, ...)
{
if(!is.function(fun)) stop("\nsolnp-->error: fun does not appear to be a function\n", call. = FALSE)
val = fun(pars, ...)
if(length(val) != 1) stop("\nsolnp-->error: objective function returns value of length greater than 1!\n", call. = FALSE)
assign(".solnp_fun", fun, envir = .env)
ctmp = get(".solnp_nfn", envir = .env)
assign(".solnp_nfn", ctmp + 1, envir = .env)
return(val)
}