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# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved.
# This code is published under the L-GPL.
#
# File: test-hs071.R
# Author: Jelmer Ypma
# Date: 10 June 2010
#
# Example problem, number 71 from the Hock-Schittkowsky test suite.
#
# \min_{x} x1*x4*(x1 + x2 + x3) + x3
# s.t.
# x1*x2*x3*x4 >= 25
# x1^2 + x2^2 + x3^2 + x4^2 = 40
# 1 <= x1,x2,x3,x4 <= 5
#
# we re-write the inequality as
# 25 - x1*x2*x3*x4 <= 0
#
# and the equality as
# x1^2 + x2^2 + x3^2 + x4^2 - 40 = 0
#
# x0 = (1,5,5,1)
#
# Optimal solution = (1.00000000, 4.74299963, 3.82114998, 1.37940829)
#
# CHANGELOG:
# 05/05/2014: Changed example to use unit testing framework testthat.
# 12/12/2019: Corrected warnings and using updated testtthat framework (Avraham Adler)
test_that( "Test HS071.", {
#
# f(x) = x1*x4*(x1 + x2 + x3) + x3
#
eval_f <- function( x ) {
return( list( "objective" = x[1]*x[4]*(x[1] + x[2] + x[3]) + x[3],
"gradient" = c( x[1] * x[4] + x[4] * (x[1] + x[2] + x[3]),
x[1] * x[4],
x[1] * x[4] + 1.0,
x[1] * (x[1] + x[2] + x[3]) ) ) )
}
# Inequality constraints.
eval_g_ineq <- function( x ) {
constr <- c( 25 - x[1] * x[2] * x[3] * x[4] )
grad <- c( -x[2]*x[3]*x[4],
-x[1]*x[3]*x[4],
-x[1]*x[2]*x[4],
-x[1]*x[2]*x[3] )
return( list( "constraints"=constr, "jacobian"=grad ) )
}
# Equality constraints.
eval_g_eq <- function( x ) {
constr <- c( x[1]^2 + x[2]^2 + x[3]^2 + x[4]^2 - 40 )
grad <- c( 2.0*x[1],
2.0*x[2],
2.0*x[3],
2.0*x[4] )
return( list( "constraints"=constr, "jacobian"=grad ) )
}
# Initial values.
x0 <- c( 1, 5, 5, 1 )
# Lower and upper bounds of control.
lb <- c( 1, 1, 1, 1 )
ub <- c( 5, 5, 5, 5 )
# Optimal solution.
solution.opt <- c(1.00000000, 4.74299963, 3.82114998, 1.37940829)
# Set optimization options.
local_opts <- list( "algorithm" = "NLOPT_LD_MMA",
"xtol_rel" = 1.0e-7 )
opts <- list( "algorithm" = "NLOPT_LD_AUGLAG",
"xtol_rel" = 1.0e-7,
"maxeval" = 1000,
"local_opts" = local_opts,
"print_level" = 0 )
# Do optimization.
res <- nloptr( x0 = x0,
eval_f = eval_f,
lb = lb,
ub = ub,
eval_g_ineq = eval_g_ineq,
eval_g_eq = eval_g_eq,
opts = opts )
# Run some checks on the optimal solution.
expect_equal(res$solution, solution.opt, tolerance = 1e-5)
expect_true(all(res$solution >= lb))
expect_true(all(res$solution <= ub))
# Check whether constraints are violated (up to specified tolerance).
expect_true( eval_g_ineq( res$solution )$constr <= res$options$tol_constraints_ineq)
expect_equal(eval_g_eq(res$solution)$constr, 0, tolerance = res$options$tol_constraints_eq)
} )
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