Greedy.LRFG.Algorithm Class Reference

Detailed Description

ALGORITHM The main implementation of the LRFG algorithm This algorithm is an implementation of the low-rank factor greedy algorithm as it was presented in the paper by A. Schmidt and B. Haasdonk (2016). It is a generic class which can be applied to all OOP models that implement a certain interface.

Definition at line 19 of file Algorithm.m.

Inheritance diagram for Greedy.LRFG.Algorithm:

Collaboration diagram for Greedy.LRFG.Algorithm:

Public Member Functions
	Algorithm (model,ModelData model_data, M_train)
	ALGORITHM Constructor for the LRFG algorithm. More...
function [ V , W , info ] =	run ()
	================================================================= RUN Implementation of the LRFG algorithm. This function actually performs the algorithm and fills the corresponding fields in detailed_data More...
function [ errs_normalized , errs_absolute , avg_time ] =	calc_error_indicator (RB_W, RB_V, mus)
	Calculate the errors for the basis stored in the matrix RB. More...

Public Attributes
	M_train
	Save the M_train class for subsequent use. More...
	error_indicator = "normalized_residual"
	Use the normalized residual as error indicator. More...
	orthonormalize_E = true
	Orthonormalize the basis wrt. to the mass matrix. More...

Protected Member Functions
function this =	initialize_basis (model,ModelData model_data)
	This function should initialize the basis with a first element. The default implementation takes the solution. More...

Protected Attributes
	model
	Pointer to the ReducedModel class.
	model_data
	The reference to the ModelData class.

Constructor & Destructor Documentation

Greedy.LRFG.Algorithm.Algorithm	(		model,
		ModelData	model_data,
			M_train
	)

ALGORITHM Constructor for the LRFG algorithm.

Parameters

model	model
model_data	Matlab structure storing (possibly) high dimensional data needed by IDetailedModel.detailed_simulation().
M_train	M train

Definition at line 74 of file Algorithm.m.

Member Function Documentation

function [ errs_normalized , errs_absolute , avg_time ] = Greedy.LRFG.Algorithm.calc_error_indicator	(		RB_W,
			RB_V,
			mus
	)

Calculate the errors for the basis stored in the matrix RB.

Parameters

RB_W	RB W
RB_V	RB V
mus	mus

Return values

errs_normalized	errs normalized
errs_absolute	errs absolute
avg_time	avg time

Definition at line 355 of file Algorithm.m.

Here is the caller graph for this function:

function this = Greedy.LRFG.Algorithm.initialize_basis (   model, ModelData  model_data  )

protected

This function should initialize the basis with a first element. The default implementation takes the solution.

Parameters

model	model
model_data	Matlab structure storing (possibly) high dimensional data needed by IDetailedModel.detailed_simulation().

Return values

this

this

Required fields of model:

• set_mu —  set mu

Generated fields of this:

• RB_V —  RB V
• RB_W —  RB W

Definition at line 113 of file Algorithm.m.

function [ V , W , info ] = Greedy.LRFG.Algorithm.run

(

)

================================================================= RUN Implementation of the LRFG algorithm. This function actually performs the algorithm and fills the corresponding fields in detailed_data

Return values

V	V
W	W
info	info

Generated fields of info:

• greedy_tolerance —  greedy tolerance
• orthonormalize_E —  orthonormalize E
• error_indicator —  error indicator
• pod_max_extension —  pod max extension
• pod_tolerance —  pod tolerance
• Z_sizes —  Z sizes
• vectors_added —  vectors added
• detailed_simulation_times —  detailed simulation times
• detailed_simulation_times_real —  detailed simulation times real
• chosen_mu —  chosen mu
• error_history_normalized —  error history normalized
• error_history_absolute —  error history absolute
• error_decay —  error decay
• num_solves —  num solves
• M_train —  M train
• runtime —  runtime

Definition at line 154 of file Algorithm.m.

Here is the call graph for this function:

Member Data Documentation

Greedy.LRFG.Algorithm.error_indicator = "normalized_residual"

Use the normalized residual as error indicator.

Default: "normalized_residual"

Definition at line 55 of file Algorithm.m.

Greedy.LRFG.Algorithm.M_train

Save the M_train class for subsequent use.

Note

This property has custom functionality when its value is changed.

Definition at line 48 of file Algorithm.m.

Greedy.LRFG.Algorithm.orthonormalize_E = true

Orthonormalize the basis wrt. to the mass matrix.

Default: true

Definition at line 64 of file Algorithm.m.

---

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

• rbasis/basisgen/oop/+Greedy/+LRFG/Algorithm.m