Bases: object
Base class for function minimization methods.
return the cost function to be minimized
Return type: | CostFunction |
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Returns a dictionary of minimizer-specific options.
Returns the result of the minimization.
Outputs a summary of the completed minimization process to the logger.
Executes the minimization algorithm for f starting with the initial guess x0.
Returns: | the result of the minimization |
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Sets a callback function to be called after every iteration. The arguments to the function are: (k, x, Jx, g_Jxx), where k is the current iteration, x is the current estimate, Jx=f(x) and g_Jxx=grad f(x).
set the cost function to be minimized
Parameters: | J (CostFunction) – the cost function to be minimized |
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Sets the maximum number of iterations before the minimizer terminates.
Sets minimizer-specific options. For a list of possible options see getOptions().
Sets the tolerance for the stopping criterion. The minimizer stops when an appropriate norm is less than m_tol.
Bases: esys.downunder.minimizers.AbstractMinimizer
Minimizer that uses the Broyden-Fletcher-Goldfarb-Shanno method.
return the cost function to be minimized
Return type: | CostFunction |
---|
Returns the result of the minimization.
Outputs a summary of the completed minimization process to the logger.
Sets a callback function to be called after every iteration. The arguments to the function are: (k, x, Jx, g_Jxx), where k is the current iteration, x is the current estimate, Jx=f(x) and g_Jxx=grad f(x).
set the cost function to be minimized
Parameters: | J (CostFunction) – the cost function to be minimized |
---|
Sets the maximum number of iterations before the minimizer terminates.
Sets the tolerance for the stopping criterion. The minimizer stops when an appropriate norm is less than m_tol.
Bases: exceptions.Exception
This is a generic exception thrown by a minimizer.
Bases: esys.downunder.minimizers.MinimizerException
Exception thrown if the iteration scheme encountered an incurable breakdown.
Bases: esys.downunder.minimizers.AbstractMinimizer
Minimizer that uses the limited-memory Broyden-Fletcher-Goldfarb-Shanno method.
return the cost function to be minimized
Return type: | CostFunction |
---|
Returns the result of the minimization.
Outputs a summary of the completed minimization process to the logger.
Sets a callback function to be called after every iteration. The arguments to the function are: (k, x, Jx, g_Jxx), where k is the current iteration, x is the current estimate, Jx=f(x) and g_Jxx=grad f(x).
set the cost function to be minimized
Parameters: | J (CostFunction) – the cost function to be minimized |
---|
Sets the maximum number of iterations before the minimizer terminates.
Sets the tolerance for the stopping criterion. The minimizer stops when an appropriate norm is less than m_tol.
Bases: esys.downunder.minimizers.MinimizerException
Exception thrown if the maximum number of iteration steps is reached.
Bases: esys.downunder.minimizers.AbstractMinimizer
Minimizer that uses the nonlinear conjugate gradient method (Fletcher-Reeves variant).
return the cost function to be minimized
Return type: | CostFunction |
---|
Returns a dictionary of minimizer-specific options.
Returns the result of the minimization.
Outputs a summary of the completed minimization process to the logger.
Sets a callback function to be called after every iteration. The arguments to the function are: (k, x, Jx, g_Jxx), where k is the current iteration, x is the current estimate, Jx=f(x) and g_Jxx=grad f(x).
set the cost function to be minimized
Parameters: | J (CostFunction) – the cost function to be minimized |
---|
Sets the maximum number of iterations before the minimizer terminates.
Sets minimizer-specific options. For a list of possible options see getOptions().
Sets the tolerance for the stopping criterion. The minimizer stops when an appropriate norm is less than m_tol.
Returns the Lsup-norm of argument arg. This is the maximum absolute value over all data points. This function is equivalent to sup(abs(arg)).
Parameters: | arg (float, int, escript.Data, numpy.ndarray) – argument |
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Returns: | maximum value of the absolute value of arg over all components and all data points |
Return type: | float |
Raises TypeError: | |
if type of arg cannot be processed |
Helper function for line_search below which tries to tighten the range alpha_lo...alpha_hi. See Chapter 3 of ‘Numerical Optimization’ by J. Nocedal for an explanation.
Line search method that satisfies the strong Wolfe conditions. See Chapter 3 of ‘Numerical Optimization’ by J. Nocedal for an explanation.
Parameters: |
|
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Returns the square root of argument arg.
Parameters: | arg (float, escript.Data, Symbol, numpy.ndarray) – argument |
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Return type: | float, escript.Data, Symbol, numpy.ndarray depending on the type of arg |
Raises TypeError: | |
if the type of the argument is not expected |