Problems for Nonlinear Least Squares and Nonlinear Equations
Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
2018 - English This report contains a description of subroutines which can be used for testing large-scale optimization codes. These subroutines can easily be obtained from the web page http://www.cs.cas.cz/~luksan/test.html. Furthermore, all test problems contained in these subroutines are presented in the analytic form. Keywords: large-scale optimization; least squares; nonlinear equations,; test problems Available in a digital repository NRGL Problems for Nonlinear Least Squares and Nonlinear Equations

This report contains a description of subroutines which can be used for testing large-scale optimization codes. These subroutines can easily be obtained from the web page ...

Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
Ústav informatiky, 2018

Robust Metalearning: Comparing Robust Regression Using A Robust Prediction Error
Peštová, Barbora; Kalina, Jan
2018 - English The aim of this paper is to construct a classification rule for predicting the best regression estimator for a new data set based on a database of 20 training data sets. Various estimators considered here include some popular methods of robust statistics. The methodology used for constructing the classification rule can be described as metalearning. Nevertheless, standard approaches of metalearning should be robustified if working with data sets contaminated by outlying measurements (outliers). Therefore, our contribution can be also described as robustification of the metalearning process by using a robust prediction error. In addition to performing the metalearning study by means of both standard and robust approaches, we search for a detailed interpretation in two particular situations. The results of detailed investigation show that the knowledge obtained by a metalearning approach standing on standard principles is prone to great variability and instability, which makes it hard to believe that the results are not just a consequence of a mere chance. Such aspect of metalearning seems not to have been previously analyzed in literature. Keywords: metalearning; robust regression; outliers; robust prediction error Fulltext is available at external website. Robust Metalearning: Comparing Robust Regression Using A Robust Prediction Error

The aim of this paper is to construct a classification rule for predicting the best regression estimator for a new data set based on a database of 20 training data sets. Various estimators considered ...

Peštová, Barbora; Kalina, Jan
Ústav informatiky, 2018

A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions
Vlček, Jan; Lukšan, Ladislav
2018 - English Keywords: Unconstrained minimization; variable metric methods; limited-memory methods; the repeated BFGS update; global convergence; numerical results Available in digital repository of the ASCR A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions

Vlček, Jan; Lukšan, Ladislav
Ústav informatiky, 2018

Numerical solution of generalized minimax problems
Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
2018 - English Keywords: Numerical optimization; nonlinear approximation; nonsmooth optimization; generalized minimax problems; recursive quadratic programming methods; interior point methods; smoothing methods; algorithms; numerical experiments Available in digital repository of the ASCR Numerical solution of generalized minimax problems

Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
Ústav informatiky, 2018

Soupis publikovaých prací pana prof. Ing. Mirko Nováka, DrSc. zpracovaný ke dni 13. dubna 2018 knihovnou Ústavu informatiky AV ČR, v. v. i. s ohledem na dostupnost uvedených prací
Nývltová, Ludmila; Ramešová, Nina; Šírová, Tereza
2018 - Czech Keywords: bibliografie Available in digital repository of the ASCR Soupis publikovaých prací pana prof. Ing. Mirko Nováka, DrSc. zpracovaný ke dni 13. dubna 2018 knihovnou Ústavu informatiky AV ČR, v. v. i. s ohledem na dostupnost uvedených prací

Nývltová, Ludmila; Ramešová, Nina; Šírová, Tereza
Ústav informatiky, 2018

An adaptive recursive multilevel approximate inverse preconditioning: Computation of the Schur complement
Kopal, Jiří; Rozložník, Miroslav; Tůma, Miroslav
2017 - English Available in digital repository of the ASCR An adaptive recursive multilevel approximate inverse preconditioning: Computation of the Schur complement

Kopal, Jiří; Rozložník, Miroslav; Tůma, Miroslav
Ústav informatiky, 2017

Exact Inference In Robust Econometrics under Heteroscedasticity
Kalina, Jan; Peštová, Barbora
2017 - English The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also the asymptotic behavior of the permutation test statistics of the Goldfeld-Quandt and Breusch-Pagan tests is investigated. A numerical experiment on real economic data is presented, which also shows how to perform a robust prediction model under heteroscedasticity. Theoretical results may be simply extended to the context of multivariate quantiles Keywords: heteroscedasticity; robust statistics; regression; diagnostic tools; economic data Fulltext is available at external website. Exact Inference In Robust Econometrics under Heteroscedasticity

The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also ...

Kalina, Jan; Peštová, Barbora
Ústav informatiky, 2017

On the Optimization of Initial Conditions for a Model Parameter Estimation
Matonoha, Ctirad; Papáček, Š.; Kindermann, S.
2017 - English The design of an experiment, e.g., the setting of initial conditions, strongly influences the accuracy of the process of determining model parameters from data. The key concept relies on the analysis of the sensitivity of the measured output with respect to the model parameters. Based on this approach we optimize an experimental design factor, the initial condition for an inverse problem of a model parameter estimation. Our approach, although case independent, is illustrated at the FRAP (Fluorescence Recovery After Photobleaching) experimental technique. The core idea resides in the maximization of a sensitivity measure, which depends on the initial condition. Numerical experiments show that the discretized optimal initial condition attains only two values. The number of jumps between these values is inversely proportional to the value of a diffusion coefficient D (characterizing the biophysical and numerical process). The smaller value of D is, the larger number of jumps occurs. Keywords: FRAP; sensitivity analysis; optimal experimental design; parameter estimation; finite differences Available in digital repository of the ASCR On the Optimization of Initial Conditions for a Model Parameter Estimation

The design of an experiment, e.g., the setting of initial conditions, strongly influences the accuracy of the process of determining model parameters from data. The key concept relies on the analysis ...

Matonoha, Ctirad; Papáček, Š.; Kindermann, S.
Ústav informatiky, 2017

UFO 2017. Interactive System for Universal Functional Optimization
Lukšan, Ladislav; Tůma, Miroslav; Matonoha, Ctirad; Vlček, Jan; Ramešová, Nina; Šiška, M.; Hartman, J.
2017 - English This report contains a description of the interactive system for universal functional optimization UFO, version 2017. This version contains interfaces to the MATLAB and SCILAB graphics environments. Keywords: numerical optimization; nonlinear programming; nonlinear approximation; algorithms; software systems Available in digital repository of the ASCR UFO 2017. Interactive System for Universal Functional Optimization

This report contains a description of the interactive system for universal functional optimization UFO, version 2017. This version contains interfaces to the MATLAB and SCILAB graphics environments.

Lukšan, Ladislav; Tůma, Miroslav; Matonoha, Ctirad; Vlček, Jan; Ramešová, Nina; Šiška, M.; Hartman, J.
Ústav informatiky, 2017

On the optimal initial conditions for an inverse problem of model parameter estimation
Matonoha, Ctirad; Papáček, Š.
2017 - English Available in digital repository of the ASCR On the optimal initial conditions for an inverse problem of model parameter estimation

Matonoha, Ctirad; Papáček, Š.
Ústav informatiky, 2017