344 lines
12 KiB
C++
344 lines
12 KiB
C++
/**
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* @file testNonlinearConstraint.cpp
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* @brief Tests for nonlinear constraints handled via SQP
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* @author Alex Cunningham
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*/
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#include <boost/bind.hpp>
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#include <CppUnitLite/TestHarness.h>
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#include <VectorConfig.h>
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#include <NonlinearConstraint.h>
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#include <NonlinearConstraint-inl.h>
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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// unary functions with scalar variables
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/* ************************************************************************* */
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namespace test1 {
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/** p = 1, gradG(x) = 2x */
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Matrix grad_g(const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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return Matrix_(1,1, 2*x);
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}
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/** p = 1, g(x) = x^2-5 = 0 */
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Vector g_func(const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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return Vector_(1, x*x-5);
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}
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} // \namespace test1
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/* ************************************************************************* */
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TEST( NonlinearConstraint1, unary_scalar_construction ) {
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// construct a constraint on x
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// the lagrange multipliers will be expected on L_x1
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// and there is only one multiplier
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size_t p = 1;
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NonlinearConstraint1<VectorConfig> c1("x", *test1::grad_g, *test1::g_func, p, "L_x1");
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// get a configuration to use for finding the error
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VectorConfig config;
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config.insert("x", Vector_(1, 1.0));
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// calculate the error
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Vector actual = c1.error_vector(config);
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Vector expected = Vector_(1.0, -4.0);
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CHECK(assert_equal(actual, expected, 1e-5));
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}
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/* ************************************************************************* */
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TEST( NonlinearConstraint1, unary_scalar_linearize ) {
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size_t p = 1;
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NonlinearConstraint1<VectorConfig> c1("x", *test1::grad_g, *test1::g_func, p, "L_x1");
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// get a configuration to use for linearization
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VectorConfig realconfig;
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realconfig.insert("x", Vector_(1, 1.0));
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// get a configuration of Lagrange multipliers
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VectorConfig lagrangeConfig;
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lagrangeConfig.insert("L_x1", Vector_(1, 3.0));
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// linearize the system
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GaussianFactor::shared_ptr actFactor, actConstraint;
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boost::tie(actFactor, actConstraint) = c1.linearize(realconfig, lagrangeConfig);
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// verify
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GaussianFactor expFactor("x", Matrix_(1,1, 6.0), "L_x1", eye(1), zero(1), 1.0);
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GaussianFactor expConstraint("x", Matrix_(1,1, 2.0), Vector_(1, 4.0), 0.0);
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CHECK(assert_equal(*actFactor, expFactor));
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CHECK(assert_equal(*actConstraint, expConstraint));
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}
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/* ************************************************************************* */
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TEST( NonlinearConstraint1, unary_scalar_equal ) {
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NonlinearConstraint1<VectorConfig>
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c1("x", *test1::grad_g, *test1::g_func, 1, "L_x1", true),
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c2("x", *test1::grad_g, *test1::g_func, 1, "L_x1"),
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c3("x", *test1::grad_g, *test1::g_func, 2, "L_x1"),
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c4("y", *test1::grad_g, *test1::g_func, 1, "L_x1");
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CHECK(assert_equal(c1, c2));
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CHECK(assert_equal(c2, c1));
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CHECK(!c1.equals(c3));
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CHECK(!c1.equals(c4));
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}
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/* ************************************************************************* */
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// binary functions with scalar variables
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/* ************************************************************************* */
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namespace test2 {
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/** gradient for x, gradG(x,y) in x: 2x*/
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Matrix grad_g1(const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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return Matrix_(1,1, 2.0*x);
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}
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/** gradient for y, gradG(x,y) in y: -1 */
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Matrix grad_g2(const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.back()](0);
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return Matrix_(1,1, -1.0);
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}
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/** p = 1, g(x) = x^2-5 -y = 0 */
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Vector g_func(const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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double y = config[keys.back()](0);
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return Vector_(1, x*x-5.0-y);
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}
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} // \namespace test2
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/* ************************************************************************* */
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TEST( NonlinearConstraint2, binary_scalar_construction ) {
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// construct a constraint on x and y
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// the lagrange multipliers will be expected on L_xy
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// and there is only one multiplier
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size_t p = 1;
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NonlinearConstraint2<VectorConfig> c1(
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"x", *test2::grad_g1,
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"y", *test2::grad_g2,
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*test2::g_func, p, "L_xy");
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// get a configuration to use for finding the error
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VectorConfig config;
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config.insert("x", Vector_(1, 1.0));
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config.insert("y", Vector_(1, 2.0));
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// calculate the error
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Vector actual = c1.error_vector(config);
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Vector expected = Vector_(1.0, -6.0);
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CHECK(assert_equal(actual, expected, 1e-5));
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}
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/* ************************************************************************* */
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TEST( NonlinearConstraint2, binary_scalar_linearize ) {
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// create a constraint
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size_t p = 1;
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NonlinearConstraint2<VectorConfig> c1(
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"x", *test2::grad_g1,
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"y", *test2::grad_g2,
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*test2::g_func, p, "L_xy");
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// get a configuration to use for finding the error
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VectorConfig realconfig;
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realconfig.insert("x", Vector_(1, 1.0));
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realconfig.insert("y", Vector_(1, 2.0));
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// get a configuration of Lagrange multipliers
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VectorConfig lagrangeConfig;
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lagrangeConfig.insert("L_xy", Vector_(1, 3.0));
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// linearize the system
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GaussianFactor::shared_ptr actFactor, actConstraint;
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boost::tie(actFactor, actConstraint) = c1.linearize(realconfig, lagrangeConfig);
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// verify
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GaussianFactor expFactor("x", Matrix_(1,1, 6.0),
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"y", Matrix_(1,1, -3.0),
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"L_xy", eye(1), zero(1), 1.0);
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GaussianFactor expConstraint("x", Matrix_(1,1, 2.0),
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"y", Matrix_(1,1, -1.0),
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Vector_(1, 6.0), 0.0);
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CHECK(assert_equal(*actFactor, expFactor));
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CHECK(assert_equal(*actConstraint, expConstraint));
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}
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/* ************************************************************************* */
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TEST( NonlinearConstraint2, binary_scalar_equal ) {
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NonlinearConstraint2<VectorConfig>
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c1("x", *test2::grad_g1, "y", *test2::grad_g2,*test2::g_func, 1, "L_xy"),
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c2("x", *test2::grad_g1, "y", *test2::grad_g2,*test2::g_func, 1, "L_xy"),
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c3("y", *test2::grad_g1, "x", *test2::grad_g2,*test2::g_func, 1, "L_xy"),
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c4("x", *test2::grad_g1, "z", *test2::grad_g2,*test2::g_func, 3, "L_xy");
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CHECK(assert_equal(c1, c2));
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CHECK(assert_equal(c2, c1));
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CHECK(!c1.equals(c3));
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CHECK(!c1.equals(c4));
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}
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/* ************************************************************************* */
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// Inequality tests
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/* ************************************************************************* */
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namespace inequality_unary {
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/** p = 1, gradG(x) = 2*x */
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Matrix grad_g(const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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return Matrix_(1,1, 2*x);
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}
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/** p = 1, g(x) x^2 - 5 > 0 */
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Vector g_func(const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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double g = x*x-5;
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return Vector_(1, g); // return the actual cost
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}
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} // \namespace test1
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/* ************************************************************************* */
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TEST( NonlinearConstraint1, unary_inequality ) {
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size_t p = 1;
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NonlinearConstraint1<VectorConfig> c1("x", *inequality_unary::grad_g,
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*inequality_unary::g_func, p, "L_x1",
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false); // inequality constraint
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// get configurations to use for evaluation
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VectorConfig config1, config2;
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config1.insert("x", Vector_(1, 10.0)); // should be inactive
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config2.insert("x", Vector_(1, 1.0)); // should have nonzero error
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// check error
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CHECK(!c1.active(config1));
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Vector actError2 = c1.error_vector(config2);
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CHECK(assert_equal(actError2, Vector_(1, -4.0, 1e-9)));
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CHECK(c1.active(config2));
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}
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/* ************************************************************************* */
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TEST( NonlinearConstraint1, unary_inequality_linearize ) {
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size_t p = 1;
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NonlinearConstraint1<VectorConfig> c1("x", *inequality_unary::grad_g,
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*inequality_unary::g_func, p, "L_x",
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false); // inequality constraint
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// get configurations to use for linearization
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VectorConfig config1, config2;
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config1.insert("x", Vector_(1, 10.0)); // should have zero error
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config2.insert("x", Vector_(1, 1.0)); // should have nonzero error
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// get a configuration of Lagrange multipliers
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VectorConfig lagrangeConfig;
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lagrangeConfig.insert("L_x", Vector_(1, 3.0));
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// linearize for inactive constraint
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GaussianFactor::shared_ptr actFactor1, actConstraint1;
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boost::tie(actFactor1, actConstraint1) = c1.linearize(config1, lagrangeConfig);
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// check if the factor is active
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CHECK(!c1.active(config1));
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// linearize for active constraint
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GaussianFactor::shared_ptr actFactor2, actConstraint2;
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boost::tie(actFactor2, actConstraint2) = c1.linearize(config2, lagrangeConfig);
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CHECK(c1.active(config2));
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// verify
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GaussianFactor expFactor("x", Matrix_(1,1, 6.0), "L_x", eye(1), zero(1), 1.0);
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GaussianFactor expConstraint("x", Matrix_(1,1, 2.0), Vector_(1, 4.0), 0.0);
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CHECK(assert_equal(*actFactor2, expFactor));
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CHECK(assert_equal(*actConstraint2, expConstraint));
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}
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/* ************************************************************************* */
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// Binding arbitrary functions
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/* ************************************************************************* */
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namespace unary_binding_functions {
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/** p = 1, gradG(x) = 2*x */
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Matrix grad_g(double coeff, const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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return Matrix_(1,1, coeff*x);
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}
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/** p = 1, g(x) x^2 - r > 0 */
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Vector g_func(double r, const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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double g = x*x-r;
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return Vector_(1, g); // return the actual cost
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}
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} // \namespace unary_binding_functions
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/* ************************************************************************* */
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TEST( NonlinearConstraint1, unary_binding ) {
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size_t p = 1;
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double coeff = 2;
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double radius = 5;
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NonlinearConstraint1<VectorConfig> c1("x",
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boost::bind(unary_binding_functions::grad_g, coeff, _1, _2),
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boost::bind(unary_binding_functions::g_func, radius, _1, _2),
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p, "L_x1",
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false); // inequality constraint
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// get configurations to use for evaluation
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VectorConfig config1, config2;
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config1.insert("x", Vector_(1, 10.0)); // should have zero error
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config2.insert("x", Vector_(1, 1.0)); // should have nonzero error
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// check error
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CHECK(!c1.active(config1));
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Vector actError2 = c1.error_vector(config2);
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CHECK(assert_equal(actError2, Vector_(1, -4.0, 1e-9)));
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CHECK(c1.active(config2));
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}
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namespace binary_binding_functions {
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/** gradient for x, gradG(x,y) in x: 2x*/
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Matrix grad_g1(double c, const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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return Matrix_(1,1, c*x);
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}
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/** gradient for y, gradG(x,y) in y: -1 */
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Matrix grad_g2(double c, const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.back()](0);
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return Matrix_(1,1, -1.0*c);
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}
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/** p = 1, g(x) = x^2-5 -y = 0 */
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Vector g_func(double r, const VectorConfig& config, const list<string>& keys) {
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double x = config[keys.front()](0);
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double y = config[keys.back()](0);
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return Vector_(1, x*x-r-y);
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}
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} // \namespace test2
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/* ************************************************************************* */
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TEST( NonlinearConstraint2, binary_binding ) {
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// construct a constraint on x and y
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// the lagrange multipliers will be expected on L_xy
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// and there is only one multiplier
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size_t p = 1;
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double a = 2.0;
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double b = 1.0;
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double r = 5.0;
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NonlinearConstraint2<VectorConfig> c1(
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"x", boost::bind(binary_binding_functions::grad_g1, a, _1, _2),
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"y", boost::bind(binary_binding_functions::grad_g2, b, _1, _2),
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boost::bind(binary_binding_functions::g_func, r, _1, _2), p, "L_xy");
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// get a configuration to use for finding the error
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VectorConfig config;
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config.insert("x", Vector_(1, 1.0));
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config.insert("y", Vector_(1, 2.0));
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// calculate the error
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Vector actual = c1.error_vector(config);
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Vector expected = Vector_(1.0, -6.0);
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CHECK(assert_equal(actual, expected, 1e-5));
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}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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