Abstract
| Original language | English |
|---|---|
| Place of Publication | Tilburg |
| Publisher | Operations research |
| Number of pages | 32 |
| Volume | 2002-64 |
| Publication status | Published - 2002 |
Publication series
| Name | CentER Discussion Paper |
|---|---|
| Volume | 2002-64 |
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Keywords
- response surface methodology
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Response Surface Methodology's Steepest Ascent and Step Size Revisited. / Kleijnen, J.P.C.; den Hertog, D.; Angun, M.E.
Tilburg : Operations research, 2002. (CentER Discussion Paper; Vol. 2002-64).Research output: Working paper › Discussion paper › Other research output
TY - UNPB
T1 - Response Surface Methodology's Steepest Ascent and Step Size Revisited
AU - Kleijnen, J.P.C.
AU - den Hertog, D.
AU - Angun, M.E.
N1 - Pagination: 32
PY - 2002
Y1 - 2002
N2 - Response Surface Methodology (RSM) searches for the input combination maximizing the output of a real system or its simulation.RSM is a heuristic that locally fits first-order polynomials, and estimates the corresponding steepest ascent (SA) paths.However, SA is scale-dependent; and its step size is selected intuitively.To tackle these two problems, this paper derives novel techniques combining mathematical statistics and mathematical programming.Technique 1 called 'adapted' SA (ASA) accounts for the covariances between the components of the estimated local gradient.ASA is scale-independent.The step-size problem is solved tentatively.Technique 2 does follow the SA direction, but with a step size inspired by ASA.Mathematical properties of the two techniques are derived and interpreted; numerical examples illustrate these properties.The search directions of the two techniques are explored in Monte Carlo experiments.These experiments show that - in general - ASA gives a better search direction than SA.
AB - Response Surface Methodology (RSM) searches for the input combination maximizing the output of a real system or its simulation.RSM is a heuristic that locally fits first-order polynomials, and estimates the corresponding steepest ascent (SA) paths.However, SA is scale-dependent; and its step size is selected intuitively.To tackle these two problems, this paper derives novel techniques combining mathematical statistics and mathematical programming.Technique 1 called 'adapted' SA (ASA) accounts for the covariances between the components of the estimated local gradient.ASA is scale-independent.The step-size problem is solved tentatively.Technique 2 does follow the SA direction, but with a step size inspired by ASA.Mathematical properties of the two techniques are derived and interpreted; numerical examples illustrate these properties.The search directions of the two techniques are explored in Monte Carlo experiments.These experiments show that - in general - ASA gives a better search direction than SA.
KW - response surface methodology
M3 - Discussion paper
VL - 2002-64
T3 - CentER Discussion Paper
BT - Response Surface Methodology's Steepest Ascent and Step Size Revisited
PB - Operations research
CY - Tilburg
ER -