Please note that the recommended version of Scilab is 2023.1.0. This page might be outdated.

See the recommended documentation of this function

# optim

non-linear optimization routine

### Calling Sequence

fopt = optim(costf, x0) fopt = optim(costf [,<contr>],x0 [,algo] [,df0 [,mem]] [,work] [,<stop>] [,<params>] [,imp=iflag]) [fopt, xopt] = optim(...) [fopt, xopt, gopt] = optim(...) [fopt, xopt, gopt, work] = optim(...)

### Arguments

- costf
a function, a list or a string, the objective function.

- x0
real vector, the initial guess for

`x`

.- <contr>
an optional sequence of arguments containing the lower and upper bounds on

`x`

. If bounds are required, this sequence of arguments must be`"b",binf,bsup`

where`binf`

and`bsup`

are real vectors with same dimension as`x0`

.- algo
a string, the algorithm to use (default

`algo="qn"`

).The available algorithms are:

`"qn"`

: Quasi-Newton with BFGS`"gc"`

: limited memory BFGS`"nd"`

: non-differentiable.The

`"nd"`

algorithm does not accept bounds on`x`

.

- df0
real scalar, a guess of the decreasing of

`f`

at first iteration. (default`df0=1`

).- mem
integer, the number of variables used to approximate the Hessian (default

`mem=10`

). This feature is available for the`"gc"`

algorithm without constraints and the non-smooth algorithm`"nd"`

without constraints.- <stop>
a sequence of arguments containing the parameters controlling the convergence of the algorithm. The following sequences are available:

"ar",nap "ar",nap,iter "ar",nap,iter,epsg "ar",nap,iter,epsg,epsf "ar",nap,iter,epsg,epsf,epsx

where:

- nap
maximum number of calls to

`costf`

allowed (default`nap=100`

).- iter
maximum number of iterations allowed (default

`iter=100`

).- epsg
threshold on gradient norm (default

`epsg= %eps`

).- epsf
threshold controlling decreasing of

`f`

(default`epsf=0`

).- epsx
threshold controlling variation of

`x`

(default`epsx=0`

). This vector (possibly matrix) with same size as`x0`

can be used to scale`x`

.

- <params>
in the case where the objective function is a C or Fortran routine, a sequence of arguments containing the method to communicate with the objective function. This option has no meaning when the cost function is a Scilab script.

The available values for <params> are the following.

`"in"`

That mode allows to allocate memory in the internal Scilab workspace so that the objective function can get arrays with the required size, but without directly allocating the memory. The

`"in"`

value stands for "initialization". In that mode, before the value and derivative of the objective function is to be computed, there is a dialog between the`optim`

Scilab primitive and the objective function`costf`

. In this dialog, the objective function is called two times, with particular values of the`ind`

parameter. The first time,`ind`

is set to 10 and the objective function is expected to set the`nizs`

,`nrzs`

and`ndzs`

integer parameters of the`nird`

common, which is defined as:common /nird/ nizs,nrzs,ndzs

This allows Scilab to allocate memory inside its internal workspace. The second time the objective function is called,

`ind`

is set to 11 and the objective function is expected to set the`ti`

,`tr`

and`tz`

arrays. After this initialization phase, each time it is called, the objective function is ensured that the`ti`

,`tr`

and`tz`

arrays which are passed to it have the values that have been previously initialized.`"ti",valti`

In this mode,

`valti`

is expected to be a Scilab vector variable containing integers. Whenever the objective function is called, the`ti`

array it receives contains the values of the Scilab variable.`"td", valtd`

In this mode,

`valtd`

is expected to be a Scilab vector variable containing double values. Whenever the objective function is called, the`td`

array it receives contains the values of the Scilab variable.`"ti",valti,"td",valtd`

This mode combines the two previous modes.

The

`ti, td`

arrays may be used so that the objective function can be computed. For example, if the objective function is a polynomial, the ti array may may be used to store the coefficients of that polynomial.Users should choose carefully between the

`"in"`

mode and the`"ti"`

and`"td"`

mode, depending on the fact that the arrays are Scilab variables or not. If the data is available as Scilab variables, then the`"ti", valti, "td", valtd`

mode should be chosen. If the data is available directly from the objective function, the`"in"`

mode should be chosen. Notice that there is no`"tr"`

mode, since, in Scilab, all real values are doubles.If neither the "in" mode, nor the "ti", "td" mode is chosen, that is, if <params> is not present as an option of the optim primitive, the user may should not assume that the ti,tr and td arrays can be used : reading or writing the arrays may generate unpredictable results.

- "imp=iflag"
named argument used to set the trace mode (default

`imp=0`

). The available values for`iflag`

are`imp=0,1,2 and >2`

.iflag=0: nothing (except errors) is reported (this is the default),

iflag=1: initial and final reports,

iflag=2: adds a report per iteration,

iflag>2: add reports on linear search.

- fopt
the value of the objective function at the point

`xopt`

- xopt
best value of

`x`

found.- gopt
the gradient of the objective function at the point

`xopt`

- work
working array for hot restart for quasi-Newton method. This array is automatically initialized by

`optim`

when`optim`

is invoked. It can be used as input parameter to speed-up the calculations.

### Description

This function solves unconstrained nonlinear optimization problems:

min f(x)

where `x`

is a vector and `f(x)`

is a function that returns a scalar. This function can also solve bound
constrained nonlinear optimization problems:

min f(x) binf <= x <= bsup

where `binf`

is the lower bound and
`bsup`

is the upper bound on `x`

.

The `costf`

argument can be a Scilab function, a
list or a string giving the name of a C or Fortran routine (see
"external"). This external must return the value `f`

of
the cost function at the point `x`

and the gradient
`g`

of the cost function at the point
`x`

.

- Scilab function case
If

`costf`

is a Scilab function, its calling sequence must be:[f, g, ind] = costf(x, ind)

where

`x`

is the current point,`ind`

is an integer flag described below,`f`

is the real value of the objective function at the point`x`

and`g`

is a vector containing the gradient of the objective function at`x`

. The variable`ind`

is described below.- List case
It may happen that objective function requires extra arguments. In this case, we can use the following feature. The

`costf`

argument can be the list`(real_costf, arg1,...,argn)`

. In this case,`real_costf`

, the first element in the list, must be a Scilab function with calling sequence:The[f,g,ind]=real_costf(x,ind,arg1,...,argn)

`x`

,`f`

,`g`

,`ind`

arguments have the same meaning as before. In this case, each time the objective function is called back, the arguments`arg1,...,argn`

are automatically appended at the end of the calling sequence of`real_costf`

.- String case
If

`costf`

is a string, it refers to the name of a C or Fortran routine which must be linked to Scilab- Fortran case
The calling sequence of the Fortran subroutine computing the objective must be:

subroutine costf(ind,n,x,f,g,ti,tr,td)

with the following declarations:

integer ind,n ti(*) double precision x(n),f,g(n),td(*) real tr(*)

The argument

`ind`

is described below.If ind = 2, 3 or 4, the inputs of the routine are :

`x, ind, n, ti, tr,td`

.If ind = 2, 3 or 4, the outputs of the routine are :

`f`

and`g`

.- C case
The calling sequence of the C function computing the objective must be:

void costf(int *ind, int *n, double *x, double *f, double *g, int *ti, float *tr, double *td)

The argument

`ind`

is described below.The inputs and outputs of the function are the same as in the fortran case.

On output, `ind<0`

means that
`f`

cannot be evaluated at `x`

and
`ind=0`

interrupts the optimization.

### Termination criteria

Each algorithm has its own termination criteria, which may use the
parameters given by the user, that is `nap`

,
`iter`

, `epsg`

, `epsf`

and `epsx`

. Not all the parameters are taken into
account. In the table below, we present the specific termination
parameters which are taken into account by each algorithm. The
unconstrained solver is identified by "UNC" while the bound constrained
solver is identified by "BND". An empty entry means that the parameter is
ignored by the algorithm.

Solver | nap | iter | epsg | epsf | epsx |

optim/"qn" UNC | X | X | X | ||

optim/"qn" BND | X | X | X | X | X |

optim/"gc" UNC | X | X | X | ||

optim/"gc" BND | X | X | X | X | X |

optim/"nd" UNC | X | X | X | X |

### Example: Scilab function

The following is an example with a Scilab function. Notice, for simplifications reasons, the Scilab function "cost" of the following example computes the objective function f and its derivative no matter of the value of ind. This allows to keep the example simple. In practical situations though, the computation of "f" and "g" may raise performances issues so that a direct optimization may be to use the value of "ind" to compute "f" and "g" only when needed.

function [f, g, ind]=cost(x, ind) xref = [1; 2; 3]; f = 0.5 * norm(x - xref)^2; g = x - xref; endfunction // Simplest call x0 = [1; -1; 1]; [fopt, xopt] = optim(cost, x0) // Use "gc" algorithm [fopt, xopt, gopt] = optim(cost, x0, "gc") // Use "nd" algorithm [fopt, xopt, gopt] = optim(cost, x0, "nd") // Upper and lower bounds on x [fopt, xopt, gopt] = optim(cost, "b", [-1;0;2], [0.5;1;4], x0) // Upper and lower bounds on x and setting up the algorithm to "gc" [fopt, xopt, gopt] = optim(cost, "b", [-1; 0; 2], [0.5; 1; 4], x0, "gc") // Bound on the number of call to the objective function [fopt, xopt, gopt] = optim(cost, "b", [-1; 0; 2], [0.5; 1; 4], x0, "gc", "ar", 3) // Set max number of call to the objective function (3) // Set max number of iterations (100) // Set stopping threshold on the value of f (1e-6), // on the value of the norm of the gradient of the objective function (1e-6) // on the improvement on the parameters x_opt (1e-6;1e-6;1e-6) [fopt, xopt, gopt] = optim(cost, "b", [-1; 0; 2], [0.5; 1; 4], x0, "gc", "ar", 3, 100, 1e-6, 1e-6, [1e-3; 1e-3; 1e-3]) // Additionnal messages are printed in the console. [fopt, xopt] = optim(cost, x0, imp = 3)

### Example: Print messages

The `imp`

flag may take negative integer values,
say k. In that case, the cost function is called once every -k iterations.
This allows to draw the function value or write a log file.

This feature is available only with the `"qn"`

algorithm without constraints.

In the following example, we solve the Rosenbrock test case. For each iteration of the algorithm, we print the value of x, f and g.

function [f, g, ind]=cost(x, ind) xref = [1; 2; 3]; f = 0.5 * norm(x - xref)^2; g = x - xref; if (ind == 1) then mprintf("f(x) = %s, |g(x)|=%s\n", string(f), string(norm(g))) end endfunction x0 = [1; -1; 1]; [fopt, xopt] = optim(cost, x0, imp = -1)

The previous script produces the following output.

-->[fopt, xopt] = optim(cost, x0, imp = -1) f(x) = 6.5, |g(x)|=3.6055513 f(x) = 2.8888889, |g(x)|=2.4037009 f(x) = 9.861D-31, |g(x)|=1.404D-15 f(x) = 0, |g(x)|=0 Norm of projected gradient lower than 0.0000000D+00. xopt = 1. 2. 3. fopt = 0.

In the following example, we solve the Rosenbrock test case. For each iteration of the algorithm, we plot the current value of x into a 2D graph containing the contours of Rosenbrock's function. This allows to see the progress of the algorithm while the algorithm is performing. We could as well write the value of x, f and g into a log file if needed.

// 1. Define Rosenbrock for optimization function [f, g, ind]=rosenbrock(x, ind) f = 100.0 *(x(2) - x(1)^2)^2 + (1 - x(1))^2; g(1) = - 400. * ( x(2) - x(1)**2 ) * x(1) -2. * ( 1. - x(1) ) g(2) = 200. * ( x(2) - x(1)**2 ) endfunction // 2. Define rosenbrock for contouring function f=rosenbrockC(x1, x2) x = [x1 x2] ind = 4 [ f , g , ind ] = rosenbrock ( x , ind ) endfunction // 3. Define Rosenbrock for plotting function [f, g, ind]=rosenbrockPlot(x, ind) [ f , g , ind ] = rosenbrock ( x , ind ) if (ind == 1) then plot ( x(1) , x(2) , "g." ) end endfunction // 4. Draw the contour of Rosenbrock's function x0 = [-1.2 1.0]; xopt = [1.0 1.0]; xdata = linspace(-2,2,100); ydata = linspace(-2,2,100); contour ( xdata , ydata , rosenbrockC , [1 10 100 500 1000]) plot(x0(1) , x0(2) , "b.") plot(xopt(1) , xopt(2) , "r*") // 5. Plot the optimization process, during optimization [fopt, xopt] = optim ( rosenbrockPlot , x0 , imp = -1)

### Example: Optimizing with numerical derivatives

It is possible to optimize a problem without an explicit knowledge of the derivative of the cost function. For this purpose, we can use the numdiff or derivative function to compute a numerical derivative of the cost function.

In the following example, we use the numdiff function to solve Rosenbrock's problem.

function f=rosenbrock(x) f = 100.0 *(x(2)-x(1)^2)^2 + (1-x(1))^2; endfunction function [f, g, ind]=rosenbrockCost(x, ind) f = rosenbrock ( x ); g= numdiff ( rosenbrock , x ); endfunction x0 = [-1.2 1.0]; [ fopt , xopt ] = optim ( rosenbrockCost , x0 )

In the following example, we use the derivative function to solve Rosenbrock's problem. Given that the step computation strategy is not the same in numdiff and derivative, this might lead to improved results.

function f=rosenbrock(x) f = 100.0 *(x(2)-x(1)^2)^2 + (1-x(1))^2; endfunction function [f, g, ind]=rosenbrockCost2(x, ind) f = rosenbrock ( x ); g = derivative ( rosenbrock , x.' , order = 4 ); endfunction x0 = [-1.2 1.0]; [fopt , xopt] = optim ( rosenbrockCost2 , x0 )

### Example: Counting function evaluations and number of iterations

The `imp`

option can take negative values. If the
`imp`

is equal to `m`

where
`m`

is a negative integer, then the cost function is
evaluated every -`m`

iterations, with the
`ind`

input argument equal to 1. The following example
uses this feature to compute the number of iterations. The global variable
`mydata`

is used to store the number of function
evaluations as well as the number of iterations.

function [f, g, ind]=cost(x, ind, xref) global _MYDATA_ if ( ind == 1 ) _MYDATA_.niter = _MYDATA_.niter + 1 end _MYDATA_.nfevals = _MYDATA_.nfevals + 1 f = 0.5 * norm(x - xref)^2; g = x - xref; endfunction xref = [1; 2; 3]; x0 = [1; -1; 1]; global _MYDATA_ _MYDATA_ = tlist ( ["T_MYDATA", "niter", "nfevals"]) _MYDATA_.niter = 0 _MYDATA_.nfevals = 0 [f, xopt] = optim(list(cost, xref), x0, imp = -1) mprintf("Number of function evaluations:%d\n", _MYDATA_.nfevals) mprintf("Number of iterations:%d\n", _MYDATA_.niter)

While the previous example perfectly works, there is a risk that the
same variable `_MYDATA_`

is used by some internal
function used by `optim`

. In this case, the value may be
wrong. This is why a sufficiently weird variable name has been
used.

### Example : Passing extra parameters

In most practical situations, the cost function depends on extra parameters which are required to evaluate the cost function. There are several methods to achieve this goal.

In the following example, the cost function uses 4 parameters
`a, b, c`

and `d`

. We define the cost
function with additionnal input arguments, which are declared after the
index argument. Then we pass a list as the first input argument of the
`optim`

solver. The first element of the list is the cost
function. The additionnal variables are directly passed to the cost
function.

function [f, g, ind]=costfunction(x, ind, a, b, c, d) f = a * ( x(1) - c ) ^2 + b * ( x(2) - d )^2 g(1) = 2 * a * ( x(1) - c ) g(2) = 2 * b * ( x(2) - d ) endfunction x0 = [1 1]; a = 1.0; b = 2.0; c = 3.0; d = 4.0; costf = list ( costfunction , a , b , c, d ); [fopt , xopt] = optim ( costf , x0 , imp = 2)

In complex cases, the cost function may have so many parameters, that having a function which takes all arguments as inputs is not convenient. For example, consider the situation where the cost function needs 12 parameters. Then, designing a function with 14 input arguments (x, index and the 12 parameters) is difficult to manage. Instead, we can use a more complex data structure to store our data. In the following example, we use a tlist to store the 4 input arguments. This method can easily be expanded to an arbitrary number of parameters.

function [f, g, ind]=costfunction(x, ind, parameters) // Get the parameters a = parameters.a b = parameters.b c = parameters.c d = parameters.d f = a * ( x(1) - c ) ^2 + b * ( x(2) - d )^2 g(1) = 2 * a * ( x(1) - c ) g(2) = 2 * b * ( x(2) - d ) endfunction x0 = [1 1]; a = 1.0; b = 2.0; c = 3.0; d = 4.0; // Store the parameters parameters = tlist ( [ "T_MYPARAMS" "a" "b" "c" "d" ]); parameters.a = a; parameters.b = b; parameters.c = c; parameters.d = d; costf = list ( costfunction , parameters ); [fopt , xopt] = optim ( costf , x0 , imp = 2)

In the following example, the parameters are defined before the optimizer is called. They are directly used in the cost function.

// The example NOT to follow function [f, g, ind]=costfunction(x, ind) f = a * ( x(1) - c ) ^2 + b * ( x(2) - d )^2 g(1) = 2 * a * ( x(1) - c ) g(2) = 2 * b * ( x(2) - d ) endfunction x0 = [1 1]; a = 1.0; b = 2.0; c = 3.0; d = 4.0; [ fopt , xopt ] = optim ( costfunction , x0 , imp = 2 )

While the previous example perfectly works, there is a risk that the
same variables are used by some internal function used by
`optim`

. In this case, the value of the parameters are
not what is expected and the optimization can fail or, worse, give a wrong
result. It is also difficult to manage such a function, which requires
that all the parameters are defined in the calling context.

In the following example, we define the cost function with the
classical header. Inside the function definition, we declare that the
parameters `a, b, c`

and `d`

are global
variables. Then we declare and set the global variables.

// Another example NOT to follow function [f, g, ind]=costfunction(x, ind) global a b c d f = a * ( x(1) - c ) ^2 + b * ( x(2) - d )^2 g(1) = 2 * a * ( x(1) - c ) g(2) = 2 * b * ( x(2) - d ) endfunction x0 = [1 1]; global a b c d a = 1.0; b = 2.0; c = 3.0; d = 4.0; [ fopt , xopt ] = optim ( costfunction , x0 , imp = 2 )

While the previous example perfectly works, there is a risk that the
same variables are used by some internal function used by
`optim`

. In this case, the value of the parameters are
not what is expected and the optimization can fail or, worse, give a wrong
result.

### Example : Checking that derivatives are correct

Many optimization problem can be avoided if the derivatives are computed correctly. One common reason for failure in the step-length procedure is an error in the calculation of the cost function and its gradient. Incorrect calculation of derivatives is by far the most common user error.

In the following example, we give a false implementation of
Rosenbrock's gradient. In order to check the computation of the
derivatives, we use the `derivative`

function. We define
the `simplified`

function, which delegates the
computation of `f`

to the rosenbrock function. The
`simplified`

function is passed as an input argument of
the `derivative`

function.

function [f, g, index]=rosenbrock(x, index) f = 100.0 *(x(2)-x(1)^2)^2 + (1-x(1))^2; // Exact : g(1) = - 400. * ( x(2) - x(1)**2 ) * x(1) -2. * ( 1. - x(1) ) // Wrong : g(1) = - 1200. * ( x(2) - x(1)**2 ) * x(1) -2. * ( 1. - x(1) ) g(2) = 200. * ( x(2) - x(1)**2 ) endfunction function f=simplified(x) index = 1; [ f , g , index ] = rosenbrock ( x , index ) endfunction x0 = [-1.2 1]; index = 1; [ f , g , index ] = rosenbrock ( x0 , index ); gnd = derivative ( simplified , x0.' ); mprintf("Exact derivative:[%s]\n" , strcat ( string(g) , " " )); mprintf("Numerical derivative:[%s]\n" , strcat ( string(gnd) , " " ));

The previous script produces the following output. Obviously, the difference between the two gradient is enormous, which shows that the wrong formula has been used in the gradient.

Exact derivative:[-638 -88] Numerical derivative:[-215.6 -88]

### Example: C function

The following is an example with a C function, where a C source code is written into a file, dynamically compiled and loaded into Scilab, and then used by the "optim" solver. The interface of the "rosenc" function is fixed, even if the arguments are not really used in the cost function. This is because the underlying optimization solvers must assume that the objective function has a known, constant interface. In the following example, the arrays ti and tr are not used, only the array "td" is used, as a parameter of the Rosenbrock function. Notice that the content of the arrays ti and td are the same that the content of the Scilab variable, as expected.

// External function written in C (C compiler required) // write down the C code (Rosenbrock problem) C=['#include <math.h>' 'double sq(double x)' '{ return x*x;}' 'void rosenc(int *ind, int *n, double *x, double *f, double *g, ' ' int *ti, float *tr, double *td)' '{' ' double p;' ' int i;' ' p=td[0];' ' if (*ind==2||*ind==4) {' ' *f=1.0;' ' for (i=1;i<*n;i++)' ' *f+=p*sq(x[i]-sq(x[i-1]))+sq(1.0-x[i]);' ' }' ' if (*ind==3||*ind==4) {' ' g[0]=-4.0*p*(x[1]-sq(x[0]))*x[0];' ' for (i=1;i<*n-1;i++)' ' g[i]=2.0*p*(x[i]-sq(x[i-1]))-4.0*p*(x[i+1]-sq(x[i]))*x[i]-2.0*(1.0-x[i]);' ' g[*n-1]=2.0*p*(x[*n-1]-sq(x[*n-2]))-2.0*(1.0-x[*n-1]);' ' }' '}']; cd TMPDIR; mputl(C, TMPDIR+'/rosenc.c') // compile the C code l = ilib_for_link('rosenc', 'rosenc.c', [], 'c'); // incremental linking link(l, 'rosenc', 'c') //solve the problem x0 = [40; 10; 50]; p = 100; [f, xo, go] = optim('rosenc', x0, 'td', p)

### Example: Fortran function

The following is an example with a Fortran function.

// External function written in Fortran (Fortran compiler required) // write down the Fortran code (Rosenbrock problem) F = [ ' subroutine rosenf(ind, n, x, f, g, ti, tr, td)' ' integer ind,n,ti(*)' ' double precision x(n),f,g(n),td(*)' ' real tr(*)' 'c' ' double precision y,p' ' p=td(1)' ' if (ind.eq.2.or.ind.eq.4) then' ' f=1.0d0' ' do i=2,n' ' f=f+p*(x(i)-x(i-1)**2)**2+(1.0d0-x(i))**2' ' enddo' ' endif' ' if (ind.eq.3.or.ind.eq.4) then' ' g(1)=-4.0d0*p*(x(2)-x(1)**2)*x(1)' ' if(n.gt.2) then' ' do i=2,n-1' ' g(i)=2.0d0*p*(x(i)-x(i-1)**2)-4.0d0*p*(x(i+1)-x(i)**2)*x(i)' ' & -2.0d0*(1.0d0-x(i))' ' enddo' ' endif' ' g(n)=2.0d0*p*(x(n)-x(n-1)**2)-2.0d0*(1.0d0-x(n))' ' endif' ' return' ' end']; cd TMPDIR; mputl(F, TMPDIR+'/rosenf.f') // compile the Fortran code l = ilib_for_link('rosenf', 'rosenf.f', [], 'f'); // incremental linking link(l, 'rosenf', 'f') //solve the problem x0 = [40; 10; 50]; p = 100; [f, xo, go] = optim('rosenf', x0, 'td', p)

### Example: Fortran function with initialization

The following is an example with a Fortran function in which the "in" option is used to allocate memory inside the Scilab environment. In this mode, there is a dialog between Scilab and the objective function. The goal of this dialog is to initialize the parameters of the objective function. Each part of this dialog is based on a specific value of the "ind" parameter.

At the beginning, Scilab calls the objective function, with the ind parameter equals to 10. This tells the objective function to initialize the sizes of the arrays it needs by setting the nizs, nrzs and ndzs integer parameters of the "nird" common. Then the objective function returns. At this point, Scilab creates internal variables and allocate memory for the variable izs, rzs and dzs. Scilab calls the objective function back again, this time with ind equals to 11. This tells the objective function to initialize the arrays izs, rzs and dzs. When the objective function has done so, it returns. Then Scilab enters in the real optimization mode and calls the optimization solver the user requested. Whenever the objective function is called, the izs, rzs and dzs arrays have the values that have been previously initialized.

// // Define a fortran source code and compile it (fortran compiler required) // fortransource = [' subroutine rosenf(ind,n,x,f,g,izs,rzs,dzs)' 'C -------------------------------------------' 'c Example of cost function given by a subroutine' 'c if n<=2 returns ind=0' 'c f.bonnans, oct 86' ' implicit double precision (a-h,o-z)' ' real rzs(1)' ' double precision dzs(*)' ' dimension x(n),g(n),izs(*)' ' common/nird/nizs,nrzs,ndzs' ' if (n.lt.3) then' ' ind=0' ' return' ' endif' ' if(ind.eq.10) then' ' nizs=2' ' nrzs=1' ' ndzs=2' ' return' ' endif' ' if(ind.eq.11) then' ' izs(1)=5' ' izs(2)=10' ' dzs(2)=100.0d+0' ' return' ' endif' ' if(ind.eq.2)go to 5' ' if(ind.eq.3)go to 20' ' if(ind.eq.4)go to 5' ' ind=-1' ' return' '5 f=1.0d+0' ' do 10 i=2,n' ' im1=i-1' '10 f=f + dzs(2)*(x(i)-x(im1)**2)**2 + (1.0d+0-x(i))**2' ' if(ind.eq.2)return' '20 g(1)=-4.0d+0*dzs(2)*(x(2)-x(1)**2)*x(1)' ' nm1=n-1' ' do 30 i=2,nm1' ' im1=i-1' ' ip1=i+1' ' g(i)=2.0d+0*dzs(2)*(x(i)-x(im1)**2)' '30 g(i)=g(i) -4.0d+0*dzs(2)*(x(ip1)-x(i)**2)*x(i) - ' ' & 2.0d+0*(1.0d+0-x(i))' ' g(n)=2.0d+0*dzs(2)*(x(n)-x(nm1)**2) - 2.0d+0*(1.0d+0-x(n))' ' return' ' end']; cd TMPDIR; mputl(fortransource, TMPDIR + '/rosenf.f') // compile the C code libpath = ilib_for_link('rosenf', 'rosenf.f', [], 'f'); // incremental linking linkid = link(libpath, 'rosenf', 'f'); x0 = 1.2 * ones(1, 5); // // Solve the problem // [f, x, g] = optim('rosenf', x0, 'in');

### Example: Fortran function with initialization on Windows with Intel Fortran Compiler

Under the Windows operating system with Intel Fortran Compiler, one must carefully design the fortran source code so that the dynamic link works properly. On Scilab's side, the optimization component is dynamically linked and the symbol "nird" is exported out of the optimization dll. On the cost function's side, which is also dynamically linked, the "nird" common must be imported in the cost function dll.

The following example is a re-writing of the previous example, with special attention for the Windows operating system with Intel Fortran compiler as example. In that case, we introduce additionnal compiling instructions, which allows the compiler to import the "nird" symbol.

fortransource = ['subroutine rosenf(ind,n,x,f,g,izs,rzs,dzs)' 'cDEC$ IF DEFINED (FORDLL)' 'cDEC$ ATTRIBUTES DLLIMPORT:: /nird/' 'cDEC$ ENDIF' 'C -------------------------------------------' 'c Example of cost function given by a subroutine' 'c if n<=2 returns ind=0' 'c f.bonnans, oct 86' ' implicit double precision (a-h,o-z)' [etc...]

### See Also

- external — Objet Scilab, fonction externe ou routine
- qpsolve — linear quadratic programming solver
- datafit — Parameter identification based on measured data
- leastsq — Solves non-linear least squares problems
- numdiff — numerical gradient estimation
- derivative — approximate derivatives of a function
- NDcost — generic external for optim computing gradient using finite differences

### References

The following is a map from the various options to the underlying solvers.

- "qn" without constraints
n1qn1 : a quasi-Newton method with a Wolfe-type line search

- "qn" with bounds constraints
qnbd : a quasi-Newton method with projection

RR-0242 - A variant of a projected variable metric method for bound constrained optimization problems, Bonnans Frederic, Rapport de recherche de l'INRIA - Rocquencourt, Octobre 1983

- "gc" without constraints
n1qn3 : a Quasi-Newton limited memory method with BFGS.

- "gc" with bounds constraints
gcbd : a BFGS-type method with limited memory and projection

- "nd" without constraints
n1fc1 : a bundle method

- "nd" with bounds constraints
not available

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