Base class for Full Batch Minimizers. More...

#include <full_batch_minimizer.hpp>

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

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

Public Member Functions
virtual	~FullBatchMinimizer ()=default

virtual V	solve (V x, VecFun< V, double > &f, GradFun< V > &Gradient)=0
	Performs optimization. More...

virtual void	setInitialHessian (const M &)
	Sets the initial Hessian approximation (if applicable). More...

virtual void	setHessian (const HessFun< V, M > &)
	Sets the Hessian function (for Second Order methods). 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...

Protected Member Functions
double	line_search (V x, V p, VecFun< V, double > &f, GradFun< V > &Gradient)
	Backtracking Line Search satisfying Wolfe Conditions. More...

Protected Attributes
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::FullBatchMinimizer< V, M >

Base class for Full Batch Minimizers.

Implements common functionalities such as Line Search for deterministic optimization.

Constructor & Destructor Documentation

◆ ~FullBatchMinimizer()

template<typename V , typename M >

virtual cpu_mlp::FullBatchMinimizer< V, M >::~FullBatchMinimizer ( )

virtualdefault

Member Function Documentation

◆ iterations()

template<typename V , typename M >

unsigned int cpu_mlp::FullBatchMinimizer< V, M >::iterations ( ) const

inline

Returns the number of iterations performed.

Returns: Iteration count.

◆ line_search()

template<typename V , typename M >

double cpu_mlp::FullBatchMinimizer< V, M >::line_search	(	V	x,
		V	p,
		VecFun< V, double > &	f,
		GradFun< V > &	Gradient
	)

inlineprotected

Backtracking Line Search satisfying Wolfe Conditions.

Parameters

x	Current position.
p	Search direction.
f	Objective function.
Gradient	Gradient function.

Returns: Step length alpha.

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◆ setArmijoMaxIter()

template<typename V , typename M >

void cpu_mlp::FullBatchMinimizer< V, M >::setArmijoMaxIter ( int max_armijo )

inline

Sets the maximum iterations for Armijo condition check (alias for setMaxLineIters).

Parameters

max_armijo Maximum iterations.

◆ setHessian()

template<typename V , typename M >

virtual void cpu_mlp::FullBatchMinimizer< V, M >::setHessian ( const HessFun< V, M > & )

inlinevirtual

Sets the Hessian function (for Second Order methods).

Parameters

hessFun Function to compute the Hessian.

Reimplemented in cpu_mlp::Newton< V, M >.

◆ setInitialHessian()

template<typename V , typename M >

virtual void cpu_mlp::FullBatchMinimizer< V, M >::setInitialHessian ( const M & )

inlinevirtual

Sets the initial Hessian approximation (if applicable).

Parameters

hess	Initial Hessian matrix.

Reimplemented in cpu_mlp::BFGS< V, M, Solver >.

◆ setMaxIterations()

template<typename V , typename M >

void cpu_mlp::FullBatchMinimizer< V, M >::setMaxIterations ( int max_iters )

inline

Sets the maximum number of iterations.

Parameters

max_iters Limit on iterations.

◆ setMaxLineIters()

template<typename V , typename M >

void cpu_mlp::FullBatchMinimizer< V, M >::setMaxLineIters ( int max_line )

inline

Sets the maximum number of iterations for the line search.

Parameters

max_line Maximum line search iterations.

◆ setRecorder()

template<typename V , typename M >

void cpu_mlp::FullBatchMinimizer< V, M >::setRecorder ( ::IterationRecorder< CpuBackend > * recorder )

inline

Attach a recorder for loss/grad history.

Parameters

recorder Recorder instance (may be null).

◆ setTolerance()

template<typename V , typename M >

void cpu_mlp::FullBatchMinimizer< V, M >::setTolerance ( double tol )

inline

Sets the tolerance for convergence.

Parameters

tol	Tolerance value (gradient norm).

◆ solve()

template<typename V , typename M >

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

pure virtual

Performs optimization.

Parameters

x	Initial guess.
f	Objective function.
Gradient	Gradient function.

Returns: Optimized parameter vector.

Implemented in cpu_mlp::Newton< V, M >, cpu_mlp::LBFGS< V, M >, cpu_mlp::GradientDescent< V, M >, and cpu_mlp::BFGS< V, M, Solver >.

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◆ solve_with_enzyme()

template<typename V , typename M >

template<auto LossFn, typename DataType >

V cpu_mlp::FullBatchMinimizer< V, M >::solve_with_enzyme	(	V	x,
		DataType *	data
	)

inline

Helper to solve directly using an Enzyme-compatible raw function.

Template Parameters

LossFn	The raw C++ function pointer for the loss .
DataType	The type of the data structure passed to the loss.

Parameters

x	Initial parameter vector.
data	Pointer to the data structure.

Returns: Optimized parameter vector.

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◆ tolerance()

template<typename V , typename M >

double cpu_mlp::FullBatchMinimizer< V, M >::tolerance ( ) const

inline

Returns the tolerance used.

Returns: Tolerance value.

Member Data Documentation

◆ _iters

template<typename V , typename M >

unsigned int cpu_mlp::FullBatchMinimizer< V, M >::_iters = 0

protected

◆ _max_iters

template<typename V , typename M >

unsigned int cpu_mlp::FullBatchMinimizer< V, M >::_max_iters = 1000

protected

◆ _tol

template<typename V , typename M >

double cpu_mlp::FullBatchMinimizer< V, M >::_tol = 1e-10

protected

◆ c1

template<typename V , typename M >

double cpu_mlp::FullBatchMinimizer< V, M >::c1 = 1e-4

protected

◆ c2

template<typename V , typename M >

double cpu_mlp::FullBatchMinimizer< V, M >::c2 = 0.9

protected

◆ max_line_iters

template<typename V , typename M >

double cpu_mlp::FullBatchMinimizer< V, M >::max_line_iters = 50

protected

◆ recorder_

template<typename V , typename M >

::IterationRecorder<CpuBackend>* cpu_mlp::FullBatchMinimizer< V, M >::recorder_ = nullptr

protected

Optional recorder for diagnostics.

◆ rho

template<typename V , typename M >

double cpu_mlp::FullBatchMinimizer< V, M >::rho = 0.5

protected

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

src/minimizer/full_batch_minimizer.hpp

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

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

Constructor & Destructor Documentation

◆ ~FullBatchMinimizer()

Member Function Documentation

◆ iterations()

◆ line_search()

◆ setArmijoMaxIter()

◆ setHessian()

◆ setInitialHessian()

◆ setMaxIterations()

◆ setMaxLineIters()

◆ setRecorder()

◆ setTolerance()

◆ solve()

◆ solve_with_enzyme()

◆ tolerance()

Member Data Documentation

◆ _iters

◆ _max_iters

◆ _tol

◆ c1

◆ c2

◆ max_line_iters

◆ recorder_

◆ rho

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