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Hi everyone,

I want to create a Gaussian PDF so I need to calculate Determinant(sigma) with sigma the covariance matrix of a gaussian variable.

If we call this variable alpha (which is a 12 dimension vector and represents the noise in a discrete dynamical equation), then sigma_ij=ExpectedValue(alpha_i*alpha_j)-ExpectedValue(alpha_i)*ExpectedValue(alpha_j)

and this is zero most of the time! So the covariance matrix is singular and the determinant is zero. 

Dear all

How to avoid in plot "matrix in singular" in my pde system

> a1 := -2;
                                     -2
> b1 := 3;
                                      3
> c1 := -1;
                                     -1
> L := 15;
                                     15
> b := .25;
                                    0.25
> th := 1.5;
                                     1.5
> pde := diff(u(x, t), x, x, x, x, t...

Which algorithm does maple follow for singular value decomposition by command 'SingularValues' in LinearAlgebra Package ?  Is it is QRSVD or divide and conquer or any other   .As far as  I know, MATLAB uses QRSVD.

Hello,

I have an error in Maple 15, I dont understand why it doesnt work. I mention that in Maple 13 it works:

The code is

  L:=4:

sum(diff( (x^2-1)^l,x$l),l=0..L);

The error is:

Error, (in SumTools:-DefiniteSum:-ClosedForm) summand is singular in the interval of summation

Somebody can help me, please?

Thank you

Hello,

I am trying to solve the following differential coupled system :

DN:=diff(N(x),x)=a/4*(x+8*(sin(F(x)))^2/x)*N(x)*diff(F(x),x)^2;

Du:=diff(u(x),x)=a/8*((x^2+8*(sin(F(x)))^2)*(1-u(x)/x)*diff(F(x),x)^2+2*(sin(F(x)))^2+4*(sin(F(x)))^4/x^2);

DDF:=diff(F(x),x$2)=1/(N(x)*(1-u(x)/x)*(x^2+8*(sin(F(x)))^2))*(-(x^2+8*(sin(F(x)))^2)*diff(N(x),x)*(1-u(x)/x)*diff(F(x),x)+(1+4*(sin(F(x)))^2/x^2+4*(1-u(x)/x)*diff(F(x),x)^2)*N(x)*sin(2*F(x...

Download for_mapleprimes.mw

I used dolve for dsystem,

but I got this error:cannot evaluate right than .....

is there anyway to overcome this problem?

thanks

For educational purposes, I was experimenting with the Fourier series expansion of periodic functions. Following the definition for 2pi-periodic functions on Wikipedia, given by

and

Hello all,

I have encountered a curious bug in the EigenConditionNumbers
procedure. In particular for a pencil pencil (A,B) with B singular,
and precision higher than hardware precision.

The following code for Digits=40 produces a Float(undefined) rather
than a Float(infinity) for the infinite eigenvalue, but an alpha and
beta that will produce an infinite eigenvalue.

Digits:=trunc(evalhf(Digits));
A:=Matrix([[1,0],[0,2]]);
B:=Matrix([[1,0],[0,0]]);

Can I solve the following two coupled differential equations instead of the previous one in Maple 13?

 with plots

Eq[1]:=3*f(eta)*(diff(f(eta), `$`(eta, 2)))-2*(diff(f(eta), eta))^2+3*g(eta)

Eq[2]:=(diff(g(eta), `$`(eta, 2)))/Pr+f(eta)*(diff(g(eta), eta))

BCs := [f(0), (D(f))(0), g(0)-1, g(25)]

pars := {Pr = 1}

for i to 2 do eq[i] := subs(pars, Eq[i]) end do

eqs := eq[1], eq[2]

vars := f(eta), g(eta)

I'm getting an error trying to get the singular vector of this matrix:

e2 := Normalize(<100,200,1>, Euclidean):
e3 := Normalize(<-500,-600,1>, Euclidean):
a := e2(1):
b := e2(2):
c := e2(3):
d := e3(1):
e := e3(2):
f := e3(3):
A := Matrix( [[a,b,c], [d,e,f], [a,e,f]] );

SingularValues(A, output='Vt');
Error, (in LinearAlgebra:-LA_Main:-SingularValues) Matrix of type complex(extended_numeric) required for output object(s) [Vt]

I've been trying to numerically solve (and then plot) this set of equations, to reproduce results from a scientific paper (Berro et al., BPJ 2007). Maple tells me that some matrix is singular - any idea what I can do differently?

Any help much appreciated.

> eqn1 := 15*(diff(theta(s), `$`(s, 2))) = N3(s)*cos(theta(s))-N1(s)*sin(theta(s));                    ...

To my mind I did all the necessary assumptions, to avoid that the summand will become singular when calculating the expected value.

What did I do wrong?

Here´s the code:

with(Statistics):
interface(showassumed=0):
assume(t::posint):
assume(x::posint):
assume(0 < q, q < 1):
X1 := RandomVariable(Geometric(q)):
DARA := t->log(t):
ExpectedValue(DARA(X1));