CppOptimizationLibrary (A header-only C++17 optimization library)

It has been now 6 years since the initial release. I did some mistakes in the previous design of this library and some features felt a bit ad-hoc. Given that C++14 is around and C++17 will become mainstream, I will take the opportunity to correct some of these mistakes and simplify things here in this v2-branch.

This branch is under development.

For the previous fully-tested version please refer to the master-branch of this repository.

Have you ever looked for a C++ function fminsearch, which is easy to use without adding tons of dependencies and without editing many setting-structs and without dependencies?

Want a full example?

    using FunctionXd = cppoptlib::function::Function<double>;

    class Rosenbrock : public FunctionXd {
      public:
        EIGEN_MAKE_ALIGNED_OPERATOR_NEW

        double operator()(const Eigen::VectorXd &x) const {
            const double t1 = (1 - x[0]);
            const double t2 = (x[1] - x[0] * x[0]);
            return   t1 * t1 + 100 * t2 * t2;
        }
    };
    int main(int argc, char const *argv[]) {
        using Solver = cppoptlib::solver::Bfgs<Rosenbrock>;

        Rosenbrock f;
        Eigen::VectorXd x(2);
        x << -1, 2;

        // Evaluate
        auto state = f.Eval(x);
        std::cout << f(x) << " = " << state.value << std::endl;
        std::cout << state.x << std::endl;
        std::cout << state.gradient << std::endl;
        if (state.hessian) {
          std::cout << *(state.hessian) << std::endl;
        }

        std::cout << cppoptlib::utils::IsGradientCorrect(f, x) << std::endl;
        std::cout << cppoptlib::utils::IsHessianCorrect(f, x) << std::endl;

        Solver solver;

        auto [solution, solver_state] = solver.Minimize(f, x);
        std::cout << "argmin " << solution.x.transpose() << std::endl;
        std::cout << "f in argmin " << solution.value << std::endl;
        std::cout << "iterations " << solver_state.num_iterations << std::endl;
        std::cout << "solver status " << solver_state.status << std::endl;
        return 0;
    }

To use another solver, simply replace BfgsSolver by another name.

Changes

Instead of nesting information in each class, this implementation will handle and update states of functions and solvers.
This will follow clang-format google-code-style and will be compliant cpplint.
This will drop Support for TensorFlow and Matlab (maybe Python will be an option).

References

L-BFGS-B: A LIMITED MEMORY ALGORITHM FOR BOUND CONSTRAINED OPTIMIZATION Richard H. Byrd, Peihuang Lu, Jorge Nocedal and Ciyou Zhu

L-BFGS: Numerical Optimization, 2nd ed. New York: Springer J. Nocedal and S. J. Wright

Citing this implementation

I see some interests in citing this implementation. Please use the following bibtex entry, if you consider to cite this implementation:

@misc{wieschollek2016cppoptimizationlibrary,
  title={CppOptimizationLibrary},
  author={Wieschollek, Patrick},
  year={2016},
  howpublished={\url{https://github.com/PatWie/CppNumericalSolvers}},
}

WeiquanWa/CppNumericalSolvers

CppOptimizationLibrary (A header-only C++17 optimization library)

Changes

References

Citing this implementation