When you use the discont option with the plot command, removable discontinuities are not plotted. See the ?plot/options help page for more details. You can get the desired plot by plotting the single point with the plots[pointplot] command and combining with plots[display]: > p1 := plot(f, -3..3, discont=true): > p2 := plots[pointplot]([1,2]): > plots[display](p1, p2); Paulina Chin Maplesoft

Could you please post the code that produced the error or send it to me directly? Thanks. Paulina Chin Maplesoft

Could you please post the code that produced the error or send it to me directly? Thanks. Paulina Chin Maplesoft

Thanks for the suggestion. We will look into updating the CurveFitting package with the newer tools currently in the Optimization and Statistics packages. Paulina Chin Maplesoft

The command GlobalSolve(p, 0..5, 0..5) should work, as long as p is a procedure that accepts two arguments and returns a float. I was able to get your example to work, though I did change the Maple procedure and variable names so that they do not conflict with the Matlab procedure and variable names: > mymat2map1 := proc(a, b) local Res, res; Matlab:-setvar("a1", a); Matlab:-setvar("b1", b); Matlab:-evalM("result=mat2map1(a1,b1)"); Res := Matlab:-getvar("result"); res := convert(Res, float); return res end proc; > GlobalSolve(mymat2map1, 0..5, 0..5, evaluationlimit=5, maximize); Paulina Chin Maplesoft

The command GlobalSolve(p, 0..5, 0..5) should work, as long as p is a procedure that accepts two arguments and returns a float. I was able to get your example to work, though I did change the Maple procedure and variable names so that they do not conflict with the Matlab procedure and variable names: > mymat2map1 := proc(a, b) local Res, res; Matlab:-setvar("a1", a); Matlab:-setvar("b1", b); Matlab:-evalM("result=mat2map1(a1,b1)"); Res := Matlab:-getvar("result"); res := convert(Res, float); return res end proc; > GlobalSolve(mymat2map1, 0..5, 0..5, evaluationlimit=5, maximize); Paulina Chin Maplesoft

Will, I'm not sure if this is what you had in mind, but there is an example on the ?plot/details page that produces a Maple leaf: > s := t->100/(100+(t-Pi/2)^8): r := t -> s(t)*(2-sin(7*t)-cos(30*t)/2): > plot([r(t),t,t=-Pi/2..3/2*Pi],numpoints=2000,coords=polar,axes=none); Paulina Chin Maplesoft

Participation varies among Maplesoft employees so I'll just speak for myself here. I try to check MaplePrimes on a regular basis and follow those discussions relevant to my work (currently plotting and optimization). Unfortunately, my regular duties don't allow me the time to participate in MaplePrimes as much as I'd like. So I leave most questions for the Maple community to answer, and so far, the community has done an admirable job. I will reply if I can contribute extra information that is not generally known among users. As Will has mentioned, we do get notified of any technical question that has not been answered for a few days. So in summary, I do read many of the posts here and appreciate any feedback that will help me improve the product, and you will see me wander into the discussion now and then. Paulina Chin Maplesoft

Alec's interpretation is correct. If you provide a weight Vector, the i-th data point is weighted by the i-th value of this Vector. Some of the information you're looking for can be found in the general regression pages, such as ?Statistics/Regression/Options. Try looking at the ?Statistics/Regression pages. This has links to additional information that applies to all the fitting commands. The nonlinear fitting routines in the Statistics package use the nonlinear least-squares solvers from the Optimization package. If you are interested in knowing more about the solvers or in passing additional options to them, you can browse the Optimization help pages. In particular, ?Optimization/Methods and ?Optimization/Computation may be useful. Paulina Chin Maplesoft

Alec's interpretation is correct. If you provide a weight Vector, the i-th data point is weighted by the i-th value of this Vector. Some of the information you're looking for can be found in the general regression pages, such as ?Statistics/Regression/Options. Try looking at the ?Statistics/Regression pages. This has links to additional information that applies to all the fitting commands. The nonlinear fitting routines in the Statistics package use the nonlinear least-squares solvers from the Optimization package. If you are interested in knowing more about the solvers or in passing additional options to them, you can browse the Optimization help pages. In particular, ?Optimization/Methods and ?Optimization/Computation may be useful. Paulina Chin Maplesoft

I too would suggest the Global Optimization Toolbox as the others have. This toolbox offers a random adaptive search method that tends to work well on problems for which one might consider a genetic algorithm. If a local solution to your optimization problem is satisfactory, then try the Optimization package that's already available with Maple 9.5 and later versions. The ?Optimization/Methods help page may be useful for selecting the best solver for your problem. In Maple 10, we added a global branch-and-bound solver for univariate problems only. As Steve points out, Maple 10 also has a new linear integer programming solver (see ?Optimization/LPSolve). Paulina Chin Maplesoft