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Scilab program examples code#
8-1/ Let ' s Scilab write the C code (in the TMPDIR directory): c_code = mputl ( c_code, TMPDIR + ' /myfunc.c ' ) // 8-2/ compiles it. You need a fortran compiler ! names = flibname = ilib_for_link ( names, " myfun.f ", , " f " ) // 7-3/ link it to scilab (see link help page) link ( flibname, names, " f " ) // 7-4/ ready for the leastsq call: be carreful do not forget to // give the dimension m after the routine name ! = leastsq ( list ( " myfun ", m, tm, ym, wm ), x0 ) // without Jacobian = leastsq ( list ( " myfun ", m, tm, ym, wm ), " mydfun ", x0 ) // with Jacobian 7-1/ Let ' s Scilab write the fortran code (in the TMPDIR directory): f_code = cd TMPDIR mputl ( f_code, TMPDIR + ' /myfun.f ' ) // 7-2/ compiles it.
Scilab program examples how to#
* exp ( - x ( 2 ) * tm ) g = endfunction // now we could call leastsq: // 1- the simplest call = leastsq ( list ( myfun, tm, ym, wm ), x0 ) // 2- we provide the Jacobian = leastsq ( list ( myfun, tm, ym, wm ), mydfun, x0 ) // a small graphic (before showing other calling features) tt = linspace ( 0, 1.1 * max ( tm ), 100 ) ' yy = yth ( tt, xopt ) scf ( ) plot ( tm, ym, " kx " ) plot ( tt, yy, " b- " ) legend ( ) xtitle ( " a simple fit with leastsq " ) // 3- how to get some information (we use imp=1) = leastsq ( 1, list ( myfun, tm, ym, wm ), mydfun, x0 ) // 4- using the conjugate gradient (instead of quasi Newton) = leastsq ( 1, list ( myfun, tm, ym, wm ), mydfun, x0, " gc " ) // 5- how to provide bound constraints (not useful here !) xinf = xsup = // without Jacobian: = leastsq ( list ( myfun, tm, ym, wm ), " b ", xinf, xsup, x0 ) // with Jacobian : = leastsq ( list ( myfun, tm, ym, wm ), mydfun, " b ", xinf, xsup, x0 ) // 6- playing with some stopping parameters of the algorithm // (allows only 40 function calls, 8 iterations and set epsg=0.01, epsf=0.1) = leastsq ( 1, list ( myfun, tm, ym, wm ), mydfun, x0, " ar ", 40, 8, 0.01, 0.1 ) * ( yth ( tm, x ) - ym ) endfunction function g = mydfun ( x, tm, ym, wm ) v = wm. This vector (possibly matrix) of same size asįunction y = yth ( t, x ) y = x ( 1 ) * exp ( - x ( 2 ) * t ) endfunction // we have the m measures (ti, yi): m = 10 tm = ' ym = ' // measure weights (here all equal to 1.) wm = ones ( m, 1 ) // and we want to find the parameters x such that the model fits the given // data in the least square sense: // // minimize f(x) = sum_i wm(i)^2 ( yth(tm(i),x) - ym(i) )^2 // initial parameters guess x0 = // in the first examples, we define the function fun and dfun // in scilab language function e = myfun ( x, tm, ym, wm ) e = wm. Sequence of optional parameters controlling the convergence of Integer, number of variables used to approximate the Hessian Most of these reports are written on the Scilab standard Imp=0 nothing (except errors) is reported, Scalar argument used to set the trace mode. Stand for quasi-Newton (default), conjugate gradient or X0 (-%inf and +%inf may be used for dimension Thoseīounds are real vectors with same dimension than The lower bound binf then by the upper bound Introduced by the string keyword 'b' followed by Real vector (initial guess of the variable to beĪ scilab function or a string defining the Jacobian matrix ofįun (see more details in DESCRIPTION). The arguments of the legend() function are string variables for each line plot, and a number which represents the position where the legend is going to be displayed on the graphic window.A scilab function or a list defining a function from If we have multiple line plots on the same axes, we need to add also a legend, in order to distinguish between the lines. It has the same syntax as the labels, and displays the string argument as a centered text above the axes. The arguments are string variables representing the text to be displayed.įor the title of the plot we use the title() function. The axes labels are added with xlabel() and ylabel() functions. The default setting for the grid are: black color and dashed lines. The grid is displayed with the function xgrid(). For this we need to enter at the Scilab console the following instructions: To have a complete plot we need to add a grid, labels for both axes, a title and a legend. With every plot() function we add, new line plots are created on the same graphic window. Bound Constrained Nonlinear Optimization: Michalewicz Function. Unconstrained Nonlinear Optimization: Steady State Temperature. Mixed-Integer Linear Optimization: Assignment Problem. Linear Optimization: Transportation problem. Linear Optimization: Product Mix Problem.
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Image: Multiple line plot on the same axes in Scilab Linear Optimization: Multistage Planning Problem.