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# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved.
# This code is published under the L-GPL.
#
# File: test-parameters.R
# Author: Jelmer Ypma
# Date: 17 August 2010
#
# Example shows how we can have an objective function
# depend on parameters or data. The objective function
# is a simple polynomial.
#
# 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 simple polyonmial where parameters are supplied as additional data.", {
# Objective function and gradient in terms of parameters.
eval_f <- function(x, params) {
return( params[1]*x^2 + params[2]*x + params[3] )
}
eval_grad_f <- function(x, params) {
return( 2*params[1]*x + params[2] )
}
# Define parameters that we want to use.
params <- c(1,2,3)
# Define initial value of the optimzation problem.
x0 <- 0
# solve using nloptr adding params as an additional parameter
res <- nloptr(
x0 = x0,
eval_f = eval_f,
eval_grad_f = eval_grad_f,
opts = list("algorithm" = "NLOPT_LD_MMA", "xtol_rel" = 1e-6),
params = params )
# Solve using algebra
# Minimize f(x) = ax^2 + bx + c.
# Optimal value for control is -b/(2a).
expect_equal(res$solution, -params[2] / (2 * params[1]), tolerance = 1e-7)
# With value of the objective function f(-b/(2a)).
expect_equal(res$objective, eval_f(-params[2] / (2*params[1]), params))
} )
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