TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

User Guide for Modularized Surrogate Model Toolbox

Tutkimustuotos

Standard

User Guide for Modularized Surrogate Model Toolbox. / Müller, Juliane.

Tampere : Tampere University of Technology, 2012. 28 s. (TUT DPub).

Tutkimustuotos

Harvard

Müller, J 2012, User Guide for Modularized Surrogate Model Toolbox. TUT DPub, Tampere University of Technology, Tampere.

APA

Müller, J. (2012). User Guide for Modularized Surrogate Model Toolbox. (TUT DPub). Tampere: Tampere University of Technology.

Vancouver

Müller J. User Guide for Modularized Surrogate Model Toolbox. Tampere: Tampere University of Technology, 2012. 28 s. (TUT DPub).

Author

Müller, Juliane. / User Guide for Modularized Surrogate Model Toolbox. Tampere : Tampere University of Technology, 2012. 28 Sivumäärä (TUT DPub).

Bibtex - Lataa

@book{c84d98b3f18e4dfdb0c6a8ed747dc291,
title = "User Guide for Modularized Surrogate Model Toolbox",
abstract = "This user guide accompanies the surrogate model toolbox for global optimization problems. The toolbox is made for computationally expensive black-box global optimization problems with continuous, integer, or mixed-integer variables. Problems where several or all variables have to be integers may also have black-box constraints, whereas purely continuous problems may only have box constraints. For problems with computationally cheap function evaluations the toolbox may not be very efficient. Surrogate models are intended to be used when function evaluations take from several minutes to several hours or more. When reading this manual it is recommended to simultaneously take a look at the code. The code is set up such that the user only has to define his/her optimization problem in a Matlab file (see Section 6.1). Additional input such as the surrogate model to be used, the sampling strategy, or starting points are optional (see Section 6). This document is structured as follows. In Section 2 the general structure of a surrogate model algorithm is summarized. The installation is described in Section 3. The dependencies of the single functions in the code are shown in Section 4. Section 5 briefly summarizes how the surrogate model algorithm works in general. Section 6 describes the options for the input of the main function. In Section 7 the input and output of the single subfunctions of the algorithm are described. Examples for using the surrogate model algorithm are given in Section 8. In Section 9 it is explained how the user can define an own (mixture) surrogate model and an example is given. The elements of the saved results are described in Section 10.",
author = "Juliane M{\"u}ller",
note = "Osa opetusmateriaalia Surrogate Model Optimization Toolbox<br/>Contribution: organisation=mat,FACT1=1",
year = "2012",
language = "English",
series = "TUT DPub",
publisher = "Tampere University of Technology",

}

RIS (suitable for import to EndNote) - Lataa

TY - BOOK

T1 - User Guide for Modularized Surrogate Model Toolbox

AU - Müller, Juliane

N1 - Osa opetusmateriaalia Surrogate Model Optimization Toolbox<br/>Contribution: organisation=mat,FACT1=1

PY - 2012

Y1 - 2012

N2 - This user guide accompanies the surrogate model toolbox for global optimization problems. The toolbox is made for computationally expensive black-box global optimization problems with continuous, integer, or mixed-integer variables. Problems where several or all variables have to be integers may also have black-box constraints, whereas purely continuous problems may only have box constraints. For problems with computationally cheap function evaluations the toolbox may not be very efficient. Surrogate models are intended to be used when function evaluations take from several minutes to several hours or more. When reading this manual it is recommended to simultaneously take a look at the code. The code is set up such that the user only has to define his/her optimization problem in a Matlab file (see Section 6.1). Additional input such as the surrogate model to be used, the sampling strategy, or starting points are optional (see Section 6). This document is structured as follows. In Section 2 the general structure of a surrogate model algorithm is summarized. The installation is described in Section 3. The dependencies of the single functions in the code are shown in Section 4. Section 5 briefly summarizes how the surrogate model algorithm works in general. Section 6 describes the options for the input of the main function. In Section 7 the input and output of the single subfunctions of the algorithm are described. Examples for using the surrogate model algorithm are given in Section 8. In Section 9 it is explained how the user can define an own (mixture) surrogate model and an example is given. The elements of the saved results are described in Section 10.

AB - This user guide accompanies the surrogate model toolbox for global optimization problems. The toolbox is made for computationally expensive black-box global optimization problems with continuous, integer, or mixed-integer variables. Problems where several or all variables have to be integers may also have black-box constraints, whereas purely continuous problems may only have box constraints. For problems with computationally cheap function evaluations the toolbox may not be very efficient. Surrogate models are intended to be used when function evaluations take from several minutes to several hours or more. When reading this manual it is recommended to simultaneously take a look at the code. The code is set up such that the user only has to define his/her optimization problem in a Matlab file (see Section 6.1). Additional input such as the surrogate model to be used, the sampling strategy, or starting points are optional (see Section 6). This document is structured as follows. In Section 2 the general structure of a surrogate model algorithm is summarized. The installation is described in Section 3. The dependencies of the single functions in the code are shown in Section 4. Section 5 briefly summarizes how the surrogate model algorithm works in general. Section 6 describes the options for the input of the main function. In Section 7 the input and output of the single subfunctions of the algorithm are described. Examples for using the surrogate model algorithm are given in Section 8. In Section 9 it is explained how the user can define an own (mixture) surrogate model and an example is given. The elements of the saved results are described in Section 10.

UR - http://urn.fi/URN:NBN:fi:tty-201210051317

M3 - Commissioned report

T3 - TUT DPub

BT - User Guide for Modularized Surrogate Model Toolbox

PB - Tampere University of Technology

CY - Tampere

ER -