Stan  2.10.0
probability, sampling & optimization
Namespaces | Functions
bfgs_linesearch.hpp File Reference
#include <boost/math/special_functions/fpclassify.hpp>
#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <string>
#include <limits>

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Namespaces

 stan
 Probability, optimization and sampling library.
 
 stan::optimization
 

Functions

template<typename Scalar >
Scalar stan::optimization::CubicInterp (const Scalar &df0, const Scalar &x1, const Scalar &f1, const Scalar &df1, const Scalar &loX, const Scalar &hiX)
 Find the minima in an interval [loX, hiX] of a cubic function which interpolates the points, function values and gradients provided. More...
 
template<typename Scalar >
Scalar stan::optimization::CubicInterp (const Scalar &x0, const Scalar &f0, const Scalar &df0, const Scalar &x1, const Scalar &f1, const Scalar &df1, const Scalar &loX, const Scalar &hiX)
 Find the minima in an interval [loX, hiX] of a cubic function which interpolates the points, function values and gradients provided. More...
 
template<typename FunctorType , typename Scalar , typename XType >
int stan::optimization::WolfeLineSearch (FunctorType &func, Scalar &alpha, XType &x1, Scalar &f1, XType &gradx1, const XType &p, const XType &x0, const Scalar &f0, const XType &gradx0, const Scalar &c1, const Scalar &c2, const Scalar &minAlpha)
 Perform a line search which finds an approximate solution to:

\[ \min_\alpha f(x_0 + \alpha p) \]

satisfying the strong Wolfe conditions: 1) $ f(x_0 + \alpha p) \leq f(x_0) + c_1 \alpha p^T g(x_0) $ 2) $ \vert p^T g(x_0 + \alpha p) \vert \leq c_2 \vert p^T g(x_0) \vert $ where $g(x) = \frac{\partial f}{\partial x}$ is the gradient of f(x). More...

 

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