| // Ceres Solver - A fast non-linear least squares minimizer | 
 | // Copyright 2023 Google Inc. All rights reserved. | 
 | // http://ceres-solver.org/ | 
 | // | 
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 | // | 
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 | // | 
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 | // | 
 | // Author: sameeragarwal@google.com (Sameer Agarwal) | 
 |  | 
 | #include "ceres/autodiff_first_order_function.h" | 
 |  | 
 | #include <memory> | 
 |  | 
 | #include "ceres/array_utils.h" | 
 | #include "ceres/first_order_function.h" | 
 | #include "gtest/gtest.h" | 
 |  | 
 | namespace ceres { | 
 | namespace internal { | 
 |  | 
 | class QuadraticCostFunctor { | 
 |  public: | 
 |   explicit QuadraticCostFunctor(double a) : a_(a) {} | 
 |   template <typename T> | 
 |   bool operator()(const T* const x, T* cost) const { | 
 |     cost[0] = x[0] * x[1] + x[2] * x[3] - a_; | 
 |     return true; | 
 |   } | 
 |  | 
 |  private: | 
 |   double a_; | 
 | }; | 
 |  | 
 | TEST(AutoDiffFirstOrderFunction, BilinearDifferentiationTest) { | 
 |   std::unique_ptr<FirstOrderFunction> function = | 
 |       std::make_unique<AutoDiffFirstOrderFunction<QuadraticCostFunctor, 4>>( | 
 |           1.0); | 
 |  | 
 |   double parameters[4] = {1.0, 2.0, 3.0, 4.0}; | 
 |   double gradient[4]; | 
 |   double cost; | 
 |  | 
 |   function->Evaluate(parameters, &cost, nullptr); | 
 |   EXPECT_EQ(cost, 13.0); | 
 |  | 
 |   cost = -1.0; | 
 |   function->Evaluate(parameters, &cost, gradient); | 
 |   EXPECT_EQ(cost, 13.0); | 
 |   EXPECT_EQ(gradient[0], parameters[1]); | 
 |   EXPECT_EQ(gradient[1], parameters[0]); | 
 |   EXPECT_EQ(gradient[2], parameters[3]); | 
 |   EXPECT_EQ(gradient[3], parameters[2]); | 
 | } | 
 |  | 
 | }  // namespace internal | 
 | }  // namespace ceres |