optimbase — Provides an abstract class for a general optimization component.
newobj = optimbase_new () this = optimbase_destroy (this) this = optimbase_configure (this,key,value) value = optimbase_cget (this,key) value = optimbase_get (this,key) this = optimbase_set ( this , key , value ) [ this , isok ] = optimbase_checkbounds ( this ) [ opt , isok ] = optimbase_checkx0 ( this ) this = optimbase_display ( this ) [ this , f , index [ , data ] ] = optimbase_function ( this , x , index [ , data ] ) [ this , f , c , index [ , data ] ] = optimbase_function ( this , x , index [ , data ] ) [ this , f , g , index [ , data ] ] = optimbase_function ( this , x , index [ , data ] ) [ this , f , g , c , gc , index [ , data ] ] = optimbase_function ( this , x , index [ , data ] ) [ this , hasbounds ] = optimbase_hasbounds ( this ) [ this , hascons ] = optimbase_hasconstraints ( this ) [ this , hasnlcons ] = optimbase_hasnlcons ( this ) value = optimbase_histget ( this , iter , key ) this = optimbase_histset ( this , iter , key , value ) this = optimbase_incriter ( this ) [ this , isfeasible ] = optimbase_isfeasible ( this , x ) this = optimbase_log (this,msg) optimbase_outputcmd ( this , state , data ) data = optimbase_outstruct ( this ) [ this , p ] = optimbase_proj2bnds ( this , x ) this = optimbase_stoplog ( this , msg ) [this , terminate , status] = optimbase_terminate (this , previousfopt , currentfopt , previousxopt , currentxopt ) this = optimbase_checkcostfun ( this ) [ this , isfeasible ] = optimbase_isinbounds ( this , x ) [ this , isfeasible ] = optimbase_isinnonlinconst ( this , x )
The goal of this component is to provide a building block for optimization methods. The goal is to provide a building block for a large class of specialized optimization methods. This component manages
the number of variables,
the minimum and maximum bounds,
the number of non linear inequality constraints,
the cost function,
the logging system,
various termination criteria,
etc...
The following is a list of features the Optimbase toolbox currently provides :
Manage cost function
optionnal additionnal argument
direct communication of the task to perform : cost function or inequality constraints
Manage various termination criteria, including
maximum number of iterations,
tolerance on function value (relative or absolute),
tolerance on x (relative or absolute),
maximum number of evaluations of cost function,
Manage the history of the convergence, including
history of function values,
history of optimum point.
Provide query features for
the status of the optimization process,
the number of iterations,
the number of function evaluations,
function value at initial point,
function value at optimal point,
the optimum parameters,
etc...
This set of commands allows to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This component manages the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the logging system, various termination criteria, the cost function, etc...
The optimization problem to solve is the following
min f(x) l_i <= x_i <= h_i, i = 1,n g_i(x) <= 0, i = 1,nbineq
where
number of variables
number of inequality constraints
The following functions are available.
Creates a new optimization object.
The new object.
Destroy the given object.
The current object.
Configure the current object with the given value for the given key.
The current object.
the key to configure. The following keys are available.
set to 1 to enable verbose logging. (default is 0)
set to 1 to enable verbose termination logging. (default is 0)
the initial guess.
the maximum number of function evalutations (default is 100). If this criteria is triggered, the status of the optimization is set to "maxfuneval".
the maximum number of iterations (default is 100). If this criteria is triggered, the status of the optimization is set to "maxiter".
the absolute tolerance for the function value (default is 0.0).
the relative tolerance for the function value (default is %eps).
the method used for the tolerance on function value in the termination criteria.
The following values are available : %t, %f (default is %f). If this criteria is triggered, the status of the optimization is set to "tolf".
the absolute tolerance on x (default is 0.0).
the relative tolerance on x (default is %eps).
the method used for the tolerance on x in the termination criteria.
The following values are available : %t, %f (default is %t). If this criteria is triggered, the status of the optimization is set to "tolx".
the objective function, which computes the value of the cost and the non linear constraints, if any.
See below for the details of the communication between the optimization system and the cost function.
an additionnal argument, passed to the cost function.
a command which is called back for output.
See below for the details of the communication between the optimization system and the output command function.
an additionnal argument, passed to the output command.
the number of variables to optimize (default is 0).
set to %t to enable the history storing (default is %f).
the minimum bounds for the parameters, as an array of values (default is empty, i.e. there are no bounds).
the maximum bounds for the parameters, as an array of values (default is empty, i.e. there are no bounds).
the number of inequality constraints (default is 0)
the name of the log file
set to %t if the algorithm uses derivatives. Default is %f.
the value.
Get the value for the given key. If the key is unknown, generates an error.
The current object.
the name of the key to quiery. The list of available keys is the same as for the optimbase_configure function.
Check if the bounds are consistent and puts an error message if not. One could generate an error, but errors are not testable with the current system.
The current object.
Returns %T if the initial guess is consistent with the bounds and the non linear inequality constraints, if any.
The current object.
Display the current settings in the console.
The current object.
Call the cost function and return the required results.
If a cost function additionnal argument is defined in current object, pass it to the function as the last argument. See below for details.
The current object.
the current point, as a column vector
what value to compute.
See below in the section "Cost function" for details on this index.
the value of the cost function
the gradient of the cost function
the nonlinear, positive, inequality constraints
the gradient of the nonlinear, positive, inequality constraints
an optionnal user-provided argument
Set the value for the given key. If the key is unknown, generates an error.
The current object.
the key to set
The following keys are available :
the number of function evaluations
the number of iterations
the x optimum
the optimum cost function value
an array, with nbiter values, containing the history of x during the iterations.
This array is available after optimization if the history storing was enabled with the -storehistory option.
an array, with nbiter values, containing the history of the function value during the iterations.
This array is available after optimization if the history storing was enabled with the -storehistory option.
the function value for the initial guess
a string containing the status of the optimization
the value to set
Get the value for the given key. If the key is unknown, generates an error. This command corresponds with options which are not available directly to the optimbase_configure function, but are computed internally.
The current object.
the name of the key to quiery.
The list of available keys is the same as the optimbase_set function.
Returns %T if current problem has bounds.
The current object.
Returns %T if current problem has bounds constraints, linear constraints or non linear constraints.
The current object.
Returns %T if current problem has non linear constraints.
The current object.
Set the history value at given iteration for the given key. If the key is unknown, generates an error.
The current object.
the iteration number to get
the name of the key to quiery.
The list of available keys is the following : "-xopt", "-fopt".
the value to set
Returns the history value at the given iteration number for the given key. If the key is unknown, generates an error.
The current object.
the iteration number to get
the name of the key to quiery.
The list of available keys is the same as the optimbase_histset function.
Increments the number of iterations.
The current object.
Returns 1 if the given point satisfies bounds constraints and inequality constraints. Returns 0 if the given point is not in the bounds. Returns -1 if the given point does not satisfies inequality constraints.
The current object.
the current point
If verbose logging is enabled, prints the given message in the console. If verbose logging is disabled, does nothing. If the -lofgile option has been set, writes the message into the file instead of writing to the console. If the console is too slow, writing into a file can be a solution, since it is very fast.
The current object.
the message to print
Calls back user's output command.
The current object.
Returns a tlist with basic optimization fields. This tlist is suitable for use as an input argument of the output function. This tlist may be enriched by children (specialize) optimization methods.
The current object.
Returns a point, which is the projection of the given point into the bounds.
The current object.
the current point
Prints the given stopping rule message if verbose termination is enabled. If verbose termination is disabled, does nothing.
The current object.
the message to print
Returns 1 if the algorithm terminates. Returns 0 if the algorithm must continue. If the -verbosetermination option is enabled, messages are printed detailing the termination intermediate steps. The optimbase_terminate function takes into account the number of iterations, the number of evaluations of the cost function, the tolerance on x and the tolerance on f. See below in the section "Termination" for more details.
The current object.
the previous value of the cost function
the current value of the cost function
the previous x optimum
the current x optimum
%t if the algorithm must terminate, %f if the algorithm must continue
if terminate = %t, the detailed status of the termination, as a string. If terminate = %f, the status is "continue".
The following status are available :
the maximum number of iterations, provided by the -maxiter option, is reached.
the maximum number of function evaluations, provided by the -maxfunevals option, is reached
the tolerance on the function value is reached. This status is associated with the -tolfunmethod, -tolfunabsolute and -tolfunrelative options.
the tolerance on x is reached. This status is associated with the -tolxmethod, -tolxabsolute and -tolxrelative options.
Check that the cost function is correctly connected. Generate an error if there is one. Takes into account for the cost function at the initial guess x0 only. Checks that all values of the index argument are valid. If there are nonlinear constraints, check that the matrix has the correct shape.
This function requires at least one call to the cost function to make the necessary checks.
The current object.
Returns isfeasible = %t if the given point satisfies bounds constraints. Returns isfeasible = %f if the given point is not in the bounds.
The current object.
a boolean
Returns isfeasible = %t if the given point satisfies the nonlinear constraints. Returns isfeasible = %f if the given point does not satisfy the nonlinear constraints.
The current object.
a boolean
The -function option allows to configure the cost function. The cost
function is used, depending on the context, to compute the cost, the
nonlinear inequality positive constraints, the gradient of the function
and the gradient of the nonlinear inequality constraints.
The cost function can also be used to produce outputs and to terminate an optimization algorithm.
In the following, the variables are
f: scalar, the objective,
g: row matrix, the gradient of the objective,
c: row matrix, the constraints,
gc: matrix, the gradient of the constraints.
Each calling sequence of the optimbase_function
function corresponds to a specific calling sequence of the user-provided
cost function.
If the -withderivatives is false and there is no nonlinear constraint, the calling sequence is
[ this , f , index ] = optimbase_function ( this , x , index )
which corresponds to the cost functions
function [ f , index ] = costf ( x , index ) function [ f , index , data ] = costf ( x , index , data ) // if there is a -costfargument
If the -withderivatives is false and there are nonlinear constraints, the calling sequence is
[ this , f , c , index ] = optimbase_function ( this , x , index )
which corresponds to the cost functions
function [ f , c , index ] = costf ( x , index ) function [ f , c , index , data ] = costf ( x , index , data ) // if there is a -costfargument
If the -withderivatives is true and there is no nonlinear constraint, the calling sequence is
[ this , f , g , index ] = optimbase_function ( this , x , index )
which corresponds to the cost functions
function [ f , g , index ] = costf ( x , index ) function [ f , g , index , data ] = costf ( x , index , data ) // if there is a -costfargument
If the -withderivatives is true and there is are nonlinear constraints, the calling sequence is
[ this , f , g , c , gc , index ] = optimbase_function ( this , x , index )
which corresponds to the cost functions
function [ f , g , c , gc , index ] = costf ( x , index ) function [ f , g , c , gc , index , data ] = costf ( x , index , data ) // if there is a -costfargument
Each calling sequence corresponds to a particular class of algorithms, including for example
unconstrained, derivative-free algorithms,
nonlinearily constrained, derivative-free algorithms,
unconstrained, derivative-based algorithms,
nonlinearilyconstrained, derivative-based algorithms,
etc...
The current component is designed in order to be able to handle many situations.
The index input parameter has the following meaning.
index = 1: nothing is to be computed, the user may display messages, for example
index = 2: compute f
index = 3: compute g
index = 4: compute f and g
index = 5: compute c
index = 6: compute f and c
index = 7: compute f, g, c and gc
The index output parameter has the following meaning.
index > 0: everything is fine,
index = 0: the optimization must stop,
index < 0: one function could not be avaluated.
The option -outputcommand allows to configure a command which is called back at the start of the optimization, at each iteration and at the end of the optimization.
The output function must have the following header
function outputcmd(state, data, myobj)
where
a string representing the current state of the algorithm. Available values are "init", "iter", "done".
a tlist containing at least the following entries
the current optimum
the current function value
the current iteration index
the number of function evaluations
a user-defined parameter.
This input parameter is defined with the -outputcommandarg option.
The output function may be used when debugging the specialized optimization algorithm, so that a verbose logging is produced. It may also be used to write one or several report files in a specialized format (ASCII, LaTeX, Excel, Hdf5, etc...). The user-defined parameter may be used in that case to store file names or logging options.
The data tlist argument may contain more fields than the current presented ones. These additionnal fields may contain values which are specific to the specialized algorithm, such as the simplex in a Nelder-Mead method, the gradient of the cost function in a BFGS method, etc...
The current component takes into account for several generic termination criterias. Specialized termination criterias should be implemented in specialized optimization algorithms, by calling the optimbase_termination function and adding external criterias, rather than by modification of this function.
The optimbase_terminate function uses a set of rules to compute if the termination occurs, which leads to an optimization status which is equal to one of the following : "continue", "maxiter", "maxfunevals", "tolf", "tolx". The set of rules is the following.
By default, the status is "continue" and the terminate flag is 0.
The number of iterations is examined and compared to the -maxiter option : if the following condition
iterations >= maxiter
is true, then the status is set to "maxiter" and terminate is set to %t.
The number of function evaluations and compared to the -maxfunevals option is examined : if the following condition
funevals >= maxfunevals
is true, then the status is set to "maxfuneval" and terminate is set to %t.
The tolerance on function value is examined depending on the value of the -tolfunmethod.
then the tolerance on f is just skipped.
if the following condition
abs(currentfopt) < tolfunrelative * abs(previousfopt) + tolfunabsolute
is true, then the status is set to "tolf" and terminate is set to %t.
The relative termination criteria on the function value works well if the function value at optimum is near zero. In that case, the function value at initial guess fx0 may be used as previousfopt.
The absolute termination criteria on the function value works if the user has an accurate idea of the optimum function value.
The tolerance on x is examined depending on the value of the -tolxmethod.
then the tolerance on x is just skipped.
if the following condition
norm(currentxopt - previousxopt) < tolxrelative * norm(currentxopt) + tolxabsolute
is true, then the status is set to "tolx" and terminate is set to %t.
The relative termination criteria on x works well if x at optimum is different from zero. In that case, the condition measures the distance between two iterates.
The absolute termination criteria on x works if the user has an accurate idea of the scale of the optimum x. If the optimum x is near 0, the relative tolerance will not work and the absolute tolerance is more appropriate.
In the following example, one searches the minimum of the 2D Rosenbrock function. One begins by defining the function "rosenbrock" which computes the Rosenbrock function. The traditionnal initial guess [-1.2 1.0] is used. The initial simplex is computed along the axes with a length equal to 0.1. The Nelder-Mead algorithm with variable simplex size is used. The verbose mode is enabled so that messages are generated during the algorithm. After the optimization is performed, the optimum is retrieved with quiery features.
function [ f , index ] = rosenbrock ( x , index ) f = 100*(x(2)-x(1)^2)^2 + (1-x(1))^2; endfunction opt = optimbase_new(); opt = optimbase_configure(opt,"-numberofvariables",2); nbvar = optimbase_cget(opt,"-numberofvariables"); opt = optimbase_configure(opt,"-function",rosenbrock); [ this , f , index ] = optimbase_function ( opt , [0.0 0.0] , index ); opt = optimbase_destroy(opt);