Please check: Finding_Chi_Version1.mw

My end goal is to find the following three expressions:

chi_1 := collect(X_A,[nnu[1],nnu[2]]);

chi_2 := collect(X_B,[nnu[1],nnu[2]]);

chi_3 := collect(X_C,[nnu[1],nnu[2]]);

I expect these three expressions to be linear combinations of random variables nu[1] (nnu[1]) and nu[2] (nnu[2]).

While calling solve(), I encounter this error:

Error, (in assuming) when calling 'SolveTools:-Engine:-Dispatch'. Received: 'badly formed input to solve: not fully algebraic'

What is exactly the issue here? If it can help you answer my doubt, that argmin expression I defined is composed by conditional means and variances which I computed as in here: conditional_distributions_Version1.mw

The two formulas I am trying to implement in Maple are conditional distribution of a multivariate normal distribution. Am I already doing any mistake in conditional_distributions_Version1.mw? An alternative interpretation of mine for these two formulas is: conditional_distributions_Version2.mw. Please check the light-blue-highlighted differences in the conditional variance calculation. This alternative interpretation leads to Finding_Chi_Version2.mw, which I also can't solve() (solver stuck in "evaluating") but at least I don't get the error mentioned above...

I am a bit lost to be honest: Is Finding_Chi_Version1 or Finding_Chi_Version2 the correct interpretation? 

Thanks!

Hi

Can anyone help me with this problem, i need maple to solve 8 non linear equations with 8 unknowns. Ive searched the entire internet, and this is what ive scripted so far. However there seems to be a problem with the solve command.

Ive copied the code beneath and posted the maple file aswell

Script:

restart;
g := 9.82;
p := 999.7;
u := 1.307*10^(-3);

F1 := 18 = x__1^2/(2*g) + x__2;

F2 := x__2 = x__3*13/0.1*x__4^2/(2*g) + x__5*35/0.05*x__1^2/(2*g) + x__6*x__4^2/(2*g) + x__7*x__1^2/(2*g);

F3 := 0.1*p*x__4/u = 0.1*p*x__4/u;

F4 := 0.05*p*x__1/u = 0.05*p*x__1/u;

F5 := 1/sqrt(x__3) = -2*log*2.51/(F3*sqrt(x__3));

F6 := 1/sqrt(x__5) = -2*log*2.51/(F4*sqrt(x__5));

F7 := x__4 = 0.05/0.1*x__1;

F8 := x__8 = x__1*Pi*0.05^2/2;
 

solve({F1, F2, F3, F4, F5, F6, F7, F8}, [x__1, x__2, x__3, x__4, x__5, x__6, x__7, x__8]);
Error, invalid input: solve expects its 1st argument, eqs, to be of type {`and`, `not`, `or`, algebraic, relation(algebraic), ({list, set})({`and`, `not`, `or`, algebraic, relation(algebraic)})}, but received {18 = 0.5091649695e-1*x__1^2+x__2, x__2 = 6.619144604*x__3*x__4^2+35.64154786*x__5*x__1^2+0.5091649695e-1*x__6*x__4^2+0.5091649695e-1*x__7*x__1^2, x__4 = .5000000000*x__1, x__8 = 0.3926990818e-2*x__1, 1/x__3^(1/2) = (-0.6563108934e-4*log/(x__3^(1/2)*x__4) = -0.6563108934e-4*log/(x__3^(1/2)*x__4)), 1/x__5^(1/2) = (-0.1312621786e-3*log/(x__5^(1/2)*x__1) = -0.1312621786e-3*log/(x__5^(1/2)*x__1)), 38244.07039*x__1 = 38244.07039*x__1, 76488.14078*x__4 = 76488.14078*x__4}
 

Dear all

I would like to compute Hardy−Littlewood maximal function : we use polar coordinate for a radial function  and then evalaute integral with respect the radius r 

Hardy_maximal_function.mw

Thank you for your help 

The Dominating set defination and Minimal dominating set definations given below

Code to generate the above.

A Function  F such that given the Graph G and vertex v as input the function should return me the number  of minimal dominating sets in the graph G contating the vertex v.

The logic to code seems to be above me Kind help some one please.

As manually doing is not that easy kind help.

I will surely acknowlege,

Kind help

How can I solve Einstein’s equation and calculus of the value of the K constant in Einstein's equation and the value of the tensor stress energy that fits in this equation?

QTBend.docxSqBend.mw

I have a n cross n matrix M I need help to write a function f say which takes the Matrix M as input function and Normalize each column of independent data.

Here normalization is  subtract by mean and divide by Standard deviation kind help if possible

If anyone has  idea of other different types of normalization please help it will help me a lot 

Kind help your ideas will all be acknowledge Please help

Hello everyone,

I am facing an issue while installing Maple on my Intel Evo laptop. The installation process starts but then it fails and I get an error message. I have tried to install it multiple times but the issue persists. I have also checked for any updates or patches but there are none available.I am not sure what could be causing this issue. Has anyone else faced a similar problem? If so, could you please share your experience and any solutions that worked for you? I would appreciate any help or suggestions on how to resolve this issue.

Thank you in advance for your time and assistance.

I read the article "ONEOptimal: A Maple Package for Generating One-Dimensional Optimal System of Finite Dimensional Lie Algebra", and also searched out in Maplesoft website, but couldn't found. Does anyone have the package?

I am trying to see if there is a way to submit maple code as a worksheet and get results back as worksheets in a cluster. 

Is it possible to read in specific parts of a bmp image? 

Given a constant gamma, gaussian random variables S[1] and S[2], and a linear combination of gaussian random variables Omega, I need to compute Exp[ Omega | S[1], S[2] ] - (gamma/2)*Var[ Omega | S[1], S[2] ]. I am not experienced in Maple. In the attached script I include many step-by-step details on what I need to do, as well as some notes where I get stuck:

150423_OptimizationProblem.mw

It would be convenient if you could directly fix this and share the working version. Thanks!

The conditional means and variance terms are calculated according to the 2D version of the script 3_gaussian_mmcdara.mw provided by @mmcdara.

As usual, I have a tricky question. There is an integral that Maple can take numerically

R0 := 1/(a-sqrt(b+c*cos(x)));

Now let's put the coefficients, e.g.

 a := 0.9; b := 4.5; c :=0.1

and take the integral from 0 to 2*Pi

R1 := evalf(int(R0, x = 0 .. 2*Pi));

Also, there is an exact analytical result that Maple gives (I give it after simplifying it to avoid division by zero for the limit x=0 and x=2*Pi)

R2:=-4*((a^2-b+c)*EllipticK(sqrt(-2*c/(b-c)))-a^2*EllipticPi(2*c/(a^2-b+c), sqrt(-2*c/(b-c))))/((a^2-b+c)*sqrt(b-c));

As it turns out, the results are completely different. In the first case -5.145818656, while for the second case -3.612771378+0.I

Moreover, If we change the coefficients to a := 0.9; b := 4.5; c := -4 then I obtain Float(undefined)+3.662506136*I and -2.362349457+3.662506117*I , respectively.

My question: how to avoid this descepancy?

According to this help page, 

However, in Maple 2023, things become strange; different branches return distinct numbers of edges: 
(33 arcs or 40 arcs?)

Download TransReduction.mw

Any bugs? 

G__0 := GraphTheory:-Digraph({[3, 1], [9, 3], [4, 9], [14, 9], [10, 4], [5, 10], [15, 10], [11, 5], [19, 11], [12, 6], [7, 12], [17, 12], [13, 7], [8, 13], [2, 8], [10, 14], [18, 14], [11, 15], [6, 19], [16, 19], [23, 19], [13, 17], [15, 18], [21, 18], [12, 16], [22, 16], [22, 23], [20, 22], [19, 21], [17, 20]}):

How to find the similarity matrix that applies A in N, B in P, C in C and B in M;
 

restart;  
with(geometry):  
with(plots):  
_EnvHorizontalName = 'x':  _EnvVerticalName = 'y':
#Vdot := proc(U, V) local i; add(U[i]*V[i], i = 1 .. 2); end proc
;

with(LinearAlgebra):
point(A, 0, 1);
point(B, 1, 1);
point(C, 1, 0);
point(E, 0, 0);
square(Sq, [A, B, C, E]);
Phi := (1 + sqrt(5))/2;
point(N, (2 - Phi)/(Phi - 1), 1);
line(BE, [B, E]);
MakeSquare(s1, [N, C, 'diagonal']);
point(M, (3 - sqrt(5))/(2*sqrt(5) - 2), (3 - sqrt(5))/(2*sqrt(5) - 2));
point(P, (1 + sqrt(5))/(2*sqrt(5) - 2), (3*sqrt(5) - 5)/(2*sqrt(5) - 2));
                               A

                               B

                               C

                               E

                               Sq

                             1   1  (1/2)
                      Phi := - + - 5     
                             2   2       

                               N

                               BE

                               s1

                               M

                               P

 display(draw([
A(color = black, symbol = solidcircle, symbolsize = 12),   
B(color = black, symbol = solidcircle, symbolsize = 12),   
C(color = black, symbol = solidcircle, symbolsize = 12),    
E(color = black, symbol = solidcircle, symbolsize = 12), 
N(color = black, symbol = solidcircle, symbolsize = 12 ),  
Sq(color=red),BE(color=green),  
s1(color = blue)]),   
textplot([[coordinates(A)[], "A"],   
[coordinates(B)[], "B"], 
[coordinates(E)[], "E"], 
[coordinates(N)[], "N"],
[coordinates(P)[], "P"],
[coordinates(M)[], "M"],   
[coordinates(C)[], "C"]], align = [above, right]), axes = none); Thank you.