cpu_mlp::Newton< V, M > Class Template Reference

Newton minimizer (full Newton) for unconstrained optimization. More...

#include <newton.hpp>

Inheritance diagram for cpu_mlp::Newton< V, M >:

Collaboration diagram for cpu_mlp::Newton< V, M >:

Public Member Functions
void	setHessian (const HessFun< V, M > &hessFun) noexcept
	Sets the Hessian function for computing the second derivative matrix. More...
V	solve (V x, VecFun< V, double > &f, GradFun< V > &Gradient) override
	Solves the unconstrained optimization problem using Newton's Method. More...
Public Member Functions inherited from cpu_mlp::FullBatchMinimizer< V, M >
virtual	~FullBatchMinimizer ()=default
virtual void	setInitialHessian (const M &)
	Sets the initial Hessian approximation (if applicable). More...
template<auto LossFn, typename DataType >
V	solve_with_enzyme (V x, DataType *data)
	Helper to solve directly using an Enzyme-compatible raw function. More...
void	setMaxIterations (int max_iters)
	Sets the maximum number of iterations. More...
void	setMaxLineIters (int max_line)
	Sets the maximum number of iterations for the line search. More...
void	setArmijoMaxIter (int max_armijo)
	Sets the maximum iterations for Armijo condition check (alias for setMaxLineIters). More...
void	setTolerance (double tol)
	Sets the tolerance for convergence. More...
unsigned int	iterations () const
	Returns the number of iterations performed. More...
double	tolerance () const
	Returns the tolerance used. More...
void	setRecorder (::IterationRecorder< CpuBackend > *recorder)
	Attach a recorder for loss/grad history. More...

Additional Inherited Members
Protected Member Functions inherited from cpu_mlp::FullBatchMinimizer< V, M >
double	line_search (V x, V p, VecFun< V, double > &f, GradFun< V > &Gradient)
	Backtracking Line Search satisfying Wolfe Conditions. More...
Protected Attributes inherited from cpu_mlp::FullBatchMinimizer< V, M >
unsigned int	_max_iters = 1000
unsigned int	_iters = 0
double	_tol = 1e-10
::IterationRecorder< CpuBackend > *	recorder_ = nullptr
	Optional recorder for diagnostics. More...
double	c1 = 1e-4
double	c2 = 0.9
double	rho = 0.5
double	max_line_iters = 50

Detailed Description

template<typename V, typename M>
class cpu_mlp::Newton< V, M >

Newton minimizer (full Newton) for unconstrained optimization.

Member Function Documentation

setHessian()

template<typename V , typename M >

void cpu_mlp::Newton< V, M >::setHessian ( const HessFun< V, M > &  hessFun )

inlinevirtualnoexcept

Sets the Hessian function for computing the second derivative matrix.

Parameters

hessFun

A function that takes a parameter vector and returns the Hessian matrix.

Reimplemented from cpu_mlp::FullBatchMinimizer< V, M >.

solve()

template<typename V , typename M >

V cpu_mlp::Newton< V, M >::solve ( V  x, VecFun< V, double > &  f, GradFun< V > &  Gradient  )

inlineoverridevirtual

Solves the unconstrained optimization problem using Newton's Method.

Parameters

x	Initial parameter vector.
f	Objective function.
Gradient	Gradient function.

Returns

The optimized parameter vector.

Implements cpu_mlp::FullBatchMinimizer< V, M >.

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The documentation for this class was generated from the following file:

• src/minimizer/newton.hpp