“Scipy”的版本间差异
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:Methods based on conjugate gradient are named with ‘cg’ in scipy. The simple conjugate gradient method to minimize a function is scipy.optimize.fmin_cg(): |
:Methods based on conjugate gradient are named with ‘cg’ in scipy. The simple conjugate gradient method to minimize a function is scipy.optimize.fmin_cg(): |
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:In scipy, scipy.optimize.fmin() implements the Nelder-Mead approach: (不太依赖于倒数) |
:In scipy, scipy.optimize.fmin() implements the Nelder-Mead approach: (不太依赖于倒数) |
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:Brute force: a grid search |
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scipy.optimize.brute() evaluates the function on a given grid of parameters and returns the parameters corresponding to the minimum value. |
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The parameters are specified with ranges given to numpy.mgrid. By default, 20 steps are taken in each direction: |
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2017年10月1日 (日) 12:54的版本
- 求函数最小值
- Methods based on conjugate gradient are named with ‘cg’ in scipy. The simple conjugate gradient method to minimize a function is scipy.optimize.fmin_cg():
- In scipy, scipy.optimize.fmin() implements the Nelder-Mead approach: (不太依赖于倒数)
- Brute force: a grid search
scipy.optimize.brute() evaluates the function on a given grid of parameters and returns the parameters corresponding to the minimum value. The parameters are specified with ranges given to numpy.mgrid. By default, 20 steps are taken in each direction: