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[upstream_utils] Upgrade to Sleipnir 0.3.2 (#8323)

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Also includes a C++ benchmark, which has a Java counterpart in #8236.
This commit is contained in:
Tyler Veness
2025-12-01 12:51:28 -08:00
committed by GitHub
parent feea24251f
commit 08784dc2d1
65 changed files with 5699 additions and 4446 deletions
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12
wpimath/src/test/native/cpp/optimization/NonlinearProblemTest.cpp
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@@ -19,7 +19,7 @@ auto Range(T start, T end, T step) {
TEST(ProblemTest, Quartic) {
testing::internal::CaptureStdout();
slp::Problem problem;
slp::Problem<double> problem;
auto x = problem.decision_variable();
x.set_value(20.0);
@@ -47,7 +47,7 @@ TEST(ProblemTest, RosenbrockWithCubicAndLineConstraint) {
// https://en.wikipedia.org/wiki/Test_functions_for_optimization#Test_functions_for_constrained_optimization
for (auto x0 : Range(-1.5, 1.5, 0.1)) {
for (auto y0 : Range(-0.5, 2.5, 0.1)) {
slp::Problem problem;
slp::Problem<double> problem;
auto x = problem.decision_variable();
x.set_value(x0);
@@ -88,7 +88,7 @@ TEST(ProblemTest, RosenbrockWithDiskConstraint) {
// https://en.wikipedia.org/wiki/Test_functions_for_optimization#Test_functions_for_constrained_optimization
for (auto x0 : Range(-1.5, 1.5, 0.1)) {
for (auto y0 : Range(-1.5, 1.5, 0.1)) {
slp::Problem problem;
slp::Problem<double> problem;
auto x = problem.decision_variable();
x.set_value(x0);
@@ -119,7 +119,7 @@ TEST(ProblemTest, RosenbrockWithDiskConstraint) {
TEST(ProblemTest, Minimum2DDistanceWithLinearConstraint) {
testing::internal::CaptureStdout();
slp::Problem problem;
slp::Problem<double> problem;
auto x = problem.decision_variable();
x.set_value(20.0);
@@ -155,7 +155,7 @@ TEST(ProblemTest, Minimum2DDistanceWithLinearConstraint) {
TEST(ProblemTest, ConflictingBounds) {
testing::internal::CaptureStdout();
slp::Problem problem;
slp::Problem<double> problem;
auto x = problem.decision_variable();
auto y = problem.decision_variable();
@@ -187,7 +187,7 @@ TEST(ProblemTest, WachterAndBieglerLineSearchFailure) {
// [1] Nocedal, J. and Wright, S. "Numerical Optimization", 2nd. ed., Ch. 19.
// Springer, 2006.
slp::Problem problem;
slp::Problem<double> problem;
auto x = problem.decision_variable();
auto s1 = problem.decision_variable();