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 Problem = Function<double, /*Order*/ 1, /*InputDim*/ 2>;
class Rosenbrock : public Problem {
public:
using vector_t = typename Problem::vector_t;
using scalar_t = typename Problem::scalar_t;
scalar_t operator()(const vector_t &x) {
const scalar_t t1 = (1 - x[0]);
const scalar_t t2 = (x[1] - x[0] * x[0]);
return t1 * t1 + 100 * t2 * t2;
}
};
int main(int argc, char const *argv[]) {
using Function = Rosenbrock;
using Solver = cppoptlib::solver::Bfgs<Function>;
Function f;
Function::vector_t 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;
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.
- 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).
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
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}},
}
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