Nonlinear Opt.: Gold section search 3: Unterschied zwischen den Versionen
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== Index of contents == | == Index of contents == | ||
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2. Mathematical formulation | 2. Mathematical formulation | ||
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3. Example for maximum | 3. Example for maximum | ||
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== Theory == | == Theory == | ||
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Golden section search is a part of unconstrained nonlinear optimization for strictly unimodal functions. This method is used to find a minimum or maximum by narrowing values in a specific interval. The extrema exists in the range. Based on the unimodal function, we assume that the local extrema is simultaneously the global extrema. In this method we do not use derivation, but rather a "step-by-step" approximation to the extrema. | Golden section search is a part of unconstrained nonlinear optimization for strictly unimodal functions. This method is used to find a minimum or maximum by narrowing values in a specific interval. The extrema exists in the range. Based on the unimodal function, we assume that the local extrema is simultaneously the global extrema. In this method we do not use derivation, but rather a "step-by-step" approximation to the extrema. | ||
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The method of the golden section search has the function <math>f:\mathbb{R}^{n} \supseteq X \rightarrow \mathbb{R}^{n}</math> | The method of the golden section search has the function <math>f:\mathbb{R}^{n} \supseteq X \rightarrow \mathbb{R}^{n}</math> | ||
based on an interval (a<sub>0</sub>,b<sub>0</sub>), where “a” is smaller than ”b”. A termination tolerance of ε is given, with the objective ε to compare the distance between “a” and “b” in each iteration. Through each iteration, the interval will be diminished by the factor λ, which is given with the value 0,618. λ will be cut from the bigger part of the interval. k = n represents the iteration steps. | based on an interval (a<sub>0</sub>,b<sub>0</sub>), where “a” is smaller than ”b”. A termination tolerance of ε is given, with the objective ε to compare the distance between “a” and “b” in each iteration. Through each iteration, the interval will be diminished by the factor λ, which is given with the value 0,618. λ will be cut from the bigger part of the interval. k = n represents the iteration steps. | ||
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| + | ''Step 0:'' Initialization | ||
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| + | Set a<sub>0</sub> := a, b<sub>0</sub>:= b, k := 0 | ||
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| + | Calculate λ<sub>k</sub> := a<sub>k</sub> + (1 – δ) * (b<sub>k</sub> – a<sub>k</sub>) | ||
| + | and µ<sub>k</sub> := a<sub>k</sub> + δ * (b<sub>k</sub> – a<sub>k</sub>) | ||
| + | as well as f(λ<sub>k</sub>) and f(µ<sub>k</sub>) | ||
Version vom 26. Juni 2013, 13:05 Uhr
Index of contents
1. Theory
2. Mathematical formulation
3. Example for maximum
4. Sources
Theory
Golden section search is a part of unconstrained nonlinear optimization for strictly unimodal functions. This method is used to find a minimum or maximum by narrowing values in a specific interval. The extrema exists in the range. Based on the unimodal function, we assume that the local extrema is simultaneously the global extrema. In this method we do not use derivation, but rather a "step-by-step" approximation to the extrema.
Mathematical formulation (for maximum)
The method of the golden section search has the function Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): f:\mathbb{R}^{n} \supseteq X \rightarrow \mathbb{R}^{n}
based on an interval (a0,b0), where “a” is smaller than ”b”. A termination tolerance of ε is given, with the objective ε to compare the distance between “a” and “b” in each iteration. Through each iteration, the interval will be diminished by the factor λ, which is given with the value 0,618. λ will be cut from the bigger part of the interval. k = n represents the iteration steps.
Step 0: Initialization
Set a0 := a, b0:= b, k := 0
Calculate λk := ak + (1 – δ) * (bk – ak)
and µk := ak + δ * (bk – ak)
as well as f(λk) and f(µk)
