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Problems for Nonlinear Least Squares and Nonlinear Equations
Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
2018 - anglický
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.
Klíčová slova:
large-scale optimization; least squares; nonlinear equations,; test problems
Plné texty jsou dostupné v digitálním repozitáři NUŠL
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 ...
Robust Metalearning: Comparing Robust Regression Using A Robust Prediction Error
Peštová, Barbora; Kalina, Jan
2018 - anglický
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.
Klíčová slova:
metalearning; robust regression; outliers; robust prediction error
Dokument je dostupný na externích webových stránkách.
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 ...
A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions
Vlček, Jan; Lukšan, Ladislav
2018 - anglický
Klíčová slova:
Unconstrained minimization; variable metric methods; limited-memory methods; the repeated BFGS update; global convergence; numerical results
Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions
Numerical solution of generalized minimax problems
Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
2018 - anglický
Klíčová slova:
Numerical optimization; nonlinear approximation; nonsmooth optimization; generalized minimax problems; recursive quadratic programming methods; interior point methods; smoothing methods; algorithms; numerical experiments
Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
Numerical solution of generalized minimax problems
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 - český
Klíčová slova:
bibliografie
Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
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
An adaptive recursive multilevel approximate inverse preconditioning: Computation of the Schur complement
Kopal, Jiří; Rozložník, Miroslav; Tůma, Miroslav
2017 - anglický
Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
An adaptive recursive multilevel approximate inverse preconditioning: Computation of the Schur complement
Exact Inference In Robust Econometrics under Heteroscedasticity
Kalina, Jan; Peštová, Barbora
2017 - anglický
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
Klíčová slova:
heteroscedasticity; robust statistics; regression; diagnostic tools; economic data
Dokument je dostupný na externích webových stránkách.
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 ...
On the Optimization of Initial Conditions for a Model Parameter Estimation
Matonoha, Ctirad; Papáček, Š.; Kindermann, S.
2017 - anglický
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.
Klíčová slova:
FRAP; sensitivity analysis; optimal experimental design; parameter estimation; finite differences
Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
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 ...
UFO 2017. Interactive System for Universal Functional Optimization
Lukšan, Ladislav; Tůma, Miroslav; Matonoha, Ctirad; Vlček, Jan; Ramešová, Nina; Šiška, M.; Hartman, J.
2017 - anglický
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.
Klíčová slova:
numerical optimization; nonlinear programming; nonlinear approximation; algorithms; software systems
Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
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.
On the optimal initial conditions for an inverse problem of model parameter estimation
Matonoha, Ctirad; Papáček, Š.
2017 - anglický
Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
On the optimal initial conditions for an inverse problem of model parameter estimation
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