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#
# Copyright 2007-2018 by the individuals mentioned in the source code history
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
library(OpenMx)
#Compare Nelder-Mead to the GD optimizer best at handling MxConstraints:
if(mxOption(NULL,"Default optimizer")!="SLSQP"){stop("SKIP")}
#The naive "soft" inequality method works reasonably well for inequalities:
foo <- mxComputeNelderMead(iniSimplexType="right", nudgeZeroStarts=FALSE,
ineqConstraintMthd="soft", doPseudoHessian=T)
#foo$verbose <- 5L
plan <- omxDefaultComputePlan()
plan$steps <- list(foo,plan$steps$RE)
#Run with SLSQP:
testmod1 <- mxModel(
"NoJacobians",
mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=0.1,labels=paste("x",1:3,sep=""),lbound=0,name="X"),
mxAlgebra( 3*X[1,1] + X[2,1] + X[3,1], name="fitfunc"),
mxMatrix(type="Full",nrow=1,ncol=3,free=F,values=c(3,1,1),name="objgrad",dimnames=list(NULL,paste("x",1:3,sep=""))),
mxConstraint(2*X[1,1] + X[2,1] + X[3,1] - 2 < 0,name="c1"),
mxConstraint(X[1,1] - X[2,1] - X[3,1] + 1 < 0,name="c2"),
mxFitFunctionAlgebra(algebra="fitfunc",gradient="objgrad")
)
testrun1 <- mxRun(testmod1)
summary(testrun1)
testrun1$output$evaluations
testrun1$output$fit
testrun1$output$constraintFunctionValues
#Run with custom NM compute plan:
testmod2 <- mxModel(
"NoJacobians",
plan,
mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=c(0,0.5,0),labels=paste("x",1:3,sep=""),lbound=0,name="X"),
mxAlgebra( 3*X[1,1] + X[2,1] + X[3,1], name="fitfunc"),
mxMatrix(type="Full",nrow=1,ncol=3,free=F,values=c(3,1,1),name="objgrad",dimnames=list(NULL,paste("x",1:3,sep=""))),
mxConstraint(2*X[1,1] + X[2,1] + X[3,1] - 2 < 0,name="c1"),
mxConstraint(X[1,1] - X[2,1] - X[3,1] + 1 < 0,name="c2"),
mxFitFunctionAlgebra(algebra="fitfunc",gradient="objgrad")
)
testrun2 <- mxRun(testmod2)
summary(testrun2)
testrun2$output$evaluations
testrun2$output$fit
#c2 is barely satisfied within feasibility tolerance:
mxEval(X[1,1] - X[2,1] - X[3,1] + 1, testrun2, T)
omxCheckCloseEnough(testrun2$output$fit+mxEval(X[1,1] - X[2,1] - X[3,1] + 1, testrun2, T), testrun1$output$fit, 1e-7)
#Backtracking:
foo <- mxComputeNelderMead(iniSimplexType="right", nudgeZeroStarts=FALSE, doPseudoHessian=T,
ineqConstraintMthd="eqMthd", eqConstraintMthd="backtrack")
#foo$verbose <- 5L
plan <- omxDefaultComputePlan()
plan$steps <- list(foo,plan$steps$RE)
testmod3 <- mxModel(
"NoJacobians",
plan,
mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=0.1,labels=paste("x",1:3,sep=""),lbound=0,name="X"),
mxAlgebra( 3*X[1,1] + X[2,1] + X[3,1], name="fitfunc"),
mxMatrix(type="Full",nrow=1,ncol=3,free=F,values=c(3,1,1),name="objgrad",dimnames=list(NULL,paste("x",1:3,sep=""))),
mxConstraint(2*X[1,1] + X[2,1] + X[3,1] - 2 < 0,name="c1"),
mxConstraint(X[1,1] - X[2,1] - X[3,1] + 1 < 0,name="c2"),
mxFitFunctionAlgebra(algebra="fitfunc",gradient="objgrad")
)
mxEval(X[1,1] - X[2,1] - X[3,1] + 1, testmod3, T) #<--More than feas tolerance
testrun3 <- mxRun(testmod3)
summary(testrun3)
testrun3$output$evaluations
testrun3$output$fit
#c2 is barely satisfied within feasibility tolerance:
mxEval(X[1,1] - X[2,1] - X[3,1] + 1, testrun3, T)
omxCheckCloseEnough(testrun3$output$fit+mxEval(X[1,1] - X[2,1] - X[3,1] + 1, testrun3, T), testrun1$output$fit, 1e-7)
#l1p:
foo <- mxComputeNelderMead(iniSimplexType="right", nudgeZeroStarts=FALSE, doPseudoHessian=T,
ineqConstraintMthd="eqMthd", eqConstraintMthd="l1p")
#foo$verbose <- 5L
plan <- omxDefaultComputePlan()
plan$steps <- list(foo,plan$steps$RE)
testmod4 <- mxModel(
"NoJacobians",
plan,
mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=0.1,labels=paste("x",1:3,sep=""),lbound=0,name="X"),
mxAlgebra( 3*X[1,1] + X[2,1] + X[3,1], name="fitfunc"),
mxMatrix(type="Full",nrow=1,ncol=3,free=F,values=c(3,1,1),name="objgrad",dimnames=list(NULL,paste("x",1:3,sep=""))),
mxConstraint(2*X[1,1] + X[2,1] + X[3,1] - 2 < 0,name="c1"),
mxConstraint(X[1,1] - X[2,1] - X[3,1] + 1 < 0,name="c2"),
mxFitFunctionAlgebra(algebra="fitfunc",gradient="objgrad")
)
testrun4 <- mxRun(testmod4)
summary(testrun4)
omxCheckCloseEnough(testrun4$output$fit+mxEval(X[1,1] - X[2,1] - X[3,1] + 1, testrun4, T), testrun1$output$fit, 1e-7)
#PseudoHessian shouldn't be calculated when using l1p:
omxCheckTrue(is.null(testrun4$compute$steps[[1]]$output$pseudoHessian))
#GDsearch:
foo <- mxComputeNelderMead(iniSimplexType="right", nudgeZeroStarts=FALSE, doPseudoHessian=T,
ineqConstraintMthd="eqMthd", eqConstraintMthd="GDsearch")
#foo$verbose <- 5L
plan <- omxDefaultComputePlan()
plan$steps <- list(foo,plan$steps$RE)
testmod5 <- mxModel(
"NoJacobians",
plan,
mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=0.1,labels=paste("x",1:3,sep=""),lbound=0,name="X"),
mxAlgebra( 3*X[1,1] + X[2,1] + X[3,1], name="fitfunc"),
mxMatrix(type="Full",nrow=1,ncol=3,free=F,values=c(3,1,1),name="objgrad",dimnames=list(NULL,paste("x",1:3,sep=""))),
mxConstraint(2*X[1,1] + X[2,1] + X[3,1] - 2 < 0,name="c1"),
mxConstraint(X[1,1] - X[2,1] - X[3,1] + 1 < 0,name="c2"),
mxFitFunctionAlgebra(algebra="fitfunc",gradient="objgrad")
)
testrun5 <- mxRun(testmod5)
summary(testrun5)
omxCheckCloseEnough(testrun5$output$fit+mxEval(X[1,1] - X[2,1] - X[3,1] + 1, testrun5, T), testrun1$output$fit, 1e-7)
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