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diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_maxiter.sce b/tests/general_tests/lsqnonlin/lsqnonlin_maxiter.sce
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+// Check for the maximum iteration
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// 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 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+options = list("MaxIter",10)
+
+//Error
+//Maximum Number of Iterations Exceeded. Output may not be optimal.
+// gradient  =
+// 
+//    512.91855  - 4714.171  
+// lambda  =
+// 
+//   lower: [0,0]
+//   upper: [0,0]
+// output  =
+// 
+//   Iterations: 10
+//   Cpu_Time: 0.12
+//   Objective_Evaluation: 11
+//   Dual_Infeasibility: 4714.171
+//   Message: "Maximum Number of Iterations Exceeded. Output may not be optimal"
+// exitflag  =
+// 
+//  1  
+// residual  =
+// 
+//    4.8006782  
+//    5.767661   
+//    6.7598659  
+//    7.8617282  
+//    9.0596638  
+//    10.40234   
+//    11.920987  
+//    13.599744  
+//    15.516066  
+//    17.701171  
+// resnorm  =
+// 
+//    1235.2439  
+// xopt  =
+// 
+//    4.9162235  
+//  - 0.5142398
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options)