Himmelblau's function: Difference between revisions
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It has one local maximum at <math>x = -0.270844 \quad</math> and <math>y = -0.923038 \quad</math> where <math>f(x,y) = 181.616 \quad</math>, and four identical local minimums: |
It has one local maximum at <math>x = -0.270844 \quad</math> and <math>y = -0.923038 \quad</math> where <math>f(x,y) = 181.616 \quad</math>, and four identical local minimums: |
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<math>f(3.0, 2.0) = 0.0 \quad</math>, |
<math>f(3.0, 2.0) = 0.0 \quad</math>, |
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<math>f(-2.805118, 3.131312) = 0.0 \quad</math>, |
<math>f(-2.805118, 3.131312) = 0.0 \quad</math>, |
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<math>f(-3.779310, -3.283186) = 0.0 \quad</math>, |
<math>f(-3.779310, -3.283186) = 0.0 \quad</math>, |
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<math>f(3.584428, -1.848126) = 0.0 \quad</math>. |
<math>f(3.584428, -1.848126) = 0.0 \quad</math>. |
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Revision as of 14:02, 9 February 2011
In mathematical optimization, the Himmelblau's function is a multi-modal function, used to test the performance of optimization algorithms. The function is defined by:
It has one local maximum at and where , and four identical local minimums: ,
,
,
.
The locations of all the minima can be found analytically, but the expressions are long and complicated.