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Here is a more direct approach: playing with Method. NMaximize[{fmymin[a0, b0, a1, b1, a2, b2] - fmyminorg[a0, b0, a1, b1, a2, b2], {0 < a2 <= a1 <= a0 < 1, 0 <= b0 <= b1 <= b2 < 1}}, {a0, b0, a1, …

It might be that the starting values are too far away from those that maximize the function. Rather than using Table one could put everything into a Do loop and use the previous estimates as the star …

Once the coding errors are fixed, the results satisfy the constraints (to what one would expect recognizing that there will be round-off error). Clear[n, objFun1, constFun1, varFun1, params, infoStk, …

This is not a satisfactory answer but maybe a start using a brute force approach. First the online help states: NMaximize always attempts to find a global maximum of f subject to the constraints g …

Essentially after picking any the two proportions for neighboring cells, the remaining two proportions are already fully determined. If you desire certain proportions for $Q_1$ and $Q_4$ or $Q_2$ and …

Expanding on @march 's comment as stated in the online documentation FindMaximum finds local maximums so good starting values are important especially in a function like what you're using that has sev …

It seems plausible that a line that connecting two points on the convex hull is the solution. (* Find points in convex hull *) chr = ConvexHullRegion[sTest][[1]]; (* Find the two points on the convex …

Consider a vector of length $n$ (with elements sorted from low to high) named $h$. I propose that the solution for the minimum value of $k$ is $$4 h_1 h_n/(h_1+h_n)^2$$ with $c_1=h_n/(h_1+h_n)$, $c_n …

I'm assuming the objective is to find "the best" set of models (all with a single common parameter) with potentially different models for each of many data sets. Using NonlinearModelFit one could fit …

I think this issue is mainly (but not completely) associated with numeric precision issues and is not a software bug. Here's why... Consider the data with just 3 observations to fit the model in the …

I wonder if the issue is just that of lack of numerical precision and/or there are multiple solutions that give the same minimum. Consider the pieces of your code as error (the function being minimiz …

This is more of an extended comment/question. Are you looking for an interactive 3D representation? Below is a very incomplete example: Manipulate[ aa = Table[-10 + 20 i/100, {i, 0, 100}]; a = (1 …

This is just an extended comment. Many times constructing a figure helps one focus on a solution. Below the gray dots are the values of Log[1/y, Abs[Sqrt[2] - x/y]], the green dots are the values wh …

You probably first want to Rationalize the equations: EQ1 = Rationalize[σ*(1 - α)^-(β + 1)*Gamma[β + 1] - 3.69348, 0]; EQ2 = Rationalize[σ*((1 - α)^-(β + 1) - 2*(2 - α)^-(β + 1))*Gamma[β + 1] - 2.0520 …

A contour plot of the condition shows that $\beta$ will need to be between 0 and around 0.5: ContourPlot[Sin[2 α + β] == 2 Sin[β], {α, 0, π/2}, {β, 0, π/2}, FrameLabel -> (Style[#, Bold, 24, Italic] …