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rbmatlab 0.10.01
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rb_test_convergence(detailed_data,model) More...
Go to the source code of this file.
Functions | |
| function [
max_test_errs , max_mu_index , min_test_errs , min_mu_index ] = | rb_test_convergence ( detailed_data, model) |
| rb_test_convergence(detailed_data,model) | |
rb_test_convergence(detailed_data,model)
Definition in file rb_test_convergence.m.
| function [ max_test_errs , max_mu_index , min_test_errs , min_mu_index ] = rb_test_convergence | ( | detailed_data, | |
| model | |||
| ) |
rb_test_convergence(detailed_data,model)
function determining the maximum and minimum test-error (linfty-l2 or estimator) for the given set of vectors mu (columns in detailed_data.RB_info.M_test) of the RB simulation with corresponding RB set. A convergence test is performed by performing this maximum and minimum detection for all numbers of RB from 1 to the complete set. the output is max_test_errs(i): the maximum test-quantity on the given mu-subset for reduced basis RB(:,1:i). The number of the mu, which incurs this maximum error is returned in max_mu_index(i). Similarly for min_test_errs and min_mu_index.
| detailed_data | detailed data |
| model | model |
| max_test_errs | max test errs |
| max_mu_index | max mu index |
| min_test_errs | min test errs |
| min_mu_index | min mu index |
RB_error_indicator — error or estimator RB_detailed_test_savepath — in case of error this path either contains the test-samples or they are generated. error_algorithm — algorithm computing the true error in case of error mode test_N_samples — (optional) number of N samples, which are tested, i.e. value 11 for a basis of size N=21 will give 11 test-results for the values N = 1,3,5,7,9,11,13,15,17,19,21 an equidistant sampling of the N-interval is realized. If not specified, all numbers 1:N are tested set_mu — set muRB_info.M_test — RB info.M test RB — RB grid — a structure containing geometry information of a mesh used for the discretizations Definition at line 1 of file rb_test_convergence.m.
1.7.4