Items tagged with jacobian

Hello,

I would like to symbolically determine the rank of a jacobian matrix. In the help, I have seen that the Rank function of the LinearAlgebra can be used for this purpose. However, when I use this function, the function doesn't allow to find the different singularities that can occur on my jacobian matrix.

Here a exemple of a jacobian matrix that I obtain on a slidercrank mechanism:

Phi := Matrix(2, 3, {(1, 1) = -l1*sin(theta(t)), (1, 2) = -1, (1, 3) = l2*cos(beta(t)), (2, 1) = l1*cos(theta(t)), (2, 2) = 0, (2, 3) = l2*sin(beta(t))})

The rank of this jaobian (Phi) gives 2 whatever the values of theta(t) and beta(t). However, if the values of  theta(t) and beta(t) are :theta(t)=Pi/2,beta(t)=0. The rank shouldn't be 2 but 1.

Is a way to obtain the symbolic calculation of the rank of a jacobian matrix which can distinguish different cases following the values of the parameters ? In others words, my dream will be to have a Rank function (or another algorithm) which can gives :
the rank is 2 if theta(t) different of Pi/2 [Pi] and beta(t)=0 [Pi] 
and otherwise 1 if ...
and perhaps 0 if ...

Thanks a lot for your help.

I let a piece of code with an example of calculation of the rank

RankMatrix.mw

This procedure calculate the equations of motions for Euclidean space and Minkowski space  with help of the Jacobian matrix.

Procedures
Calculation the equation of motions for Euclidean space and Minkowski space

"EQM := proc(eq, g,xup,xa,xu , eta ,var)"

Calling Sequence

 

EQM(eq, g, xup, xa, xu, eta, var)

Parameters

 

parameterSequence

-

eq, g, xup, xa, xu, eta, var

eq

out

equation of motion

g

out

metric

xup

out

velocitiy vector

xa

in

position vector

xu

in

vector of the independet coortinates

eta

in

signature matrix for Minkowski space

var

in

independet variable

 

``

 Procedur Code

 

restart; with(linalg); EQM := proc (eq, g, xup, xa, xu, eta, var) local J, Jp, xdd, l, xupp, ndim; ndim := vectdim(xu); xup := vector(ndim); xupp := vector(ndim); for l to ndim do xup[l] := diff(xu[l](var), var); xupp[l] := diff(diff(xu[l](var), var), var) end do; J := jacobian(xa, xu); g := multiply(transpose(J), eta, J); g := map(simplify, g); Jp := jacobian(multiply(J, xup), xu); Jp := map(simplify, Jp); xdd := multiply(inverse(g), transpose(J), eta, Jp, xup); xdd := map(simplify, xdd); xdd := map(convert, xdd, diff); eq := vector(vectdim(xupp)); for l to ndim do eq[l] := xupp[l]+xdd[l] = 0 end do end proc

``

Input

 

xa := Vector(3, {(1) = R*sin(`ϕ`)*cos(`ϑ`), (2) = R*sin(`ϕ`)*sin(`ϑ`), (3) = R*cos(`ϕ`)}); xu := Vector(2, {(1) = `ϕ`, (2) = `ϑ`}); eta := Matrix(3, 3, {(1, 1) = 1, (1, 2) = 0, (1, 3) = 0, (2, 1) = 0, (2, 2) = 1, (2, 3) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = 1})

 

EQM(eq, g, xup, xa, xu, eta, t):

Output EOM

 

for i to vectdim(xu) do eq[i] end do;

diff(diff(`ϕ`(t), t), t)-cos(`ϕ`)*sin(`ϕ`)*(diff(`ϑ`(t), t))^2 = 0

 

diff(diff(`ϑ`(t), t), t)+2*cos(`ϕ`)*(diff(`ϑ`(t), t))*(diff(`ϕ`(t), t))/sin(`ϕ`) = 0

(5.1)

Output Line-Element

 

ds2 := expand(multiply(transpose(xup), g, xup));

(diff(`ϕ`(t), t))^2*R^2+(diff(`ϑ`(t), t))^2*R^2-(diff(`ϑ`(t), t))^2*R^2*cos(`ϕ`)^2

(6.1)

Output Metric

 

assume(cos(`ϕ`)^2 = 1-sin(`ϕ`)^2); g := map(simplify, g)

array( 1 .. 2, 1 .. 2, [( 2, 2 ) = (R^2*sin(`ϕ`)^2), ( 1, 2 ) = (0), ( 2, 1 ) = (0), ( 1, 1 ) = (R^2)  ] )

(7.1)

``

``

 

Download bsp_jacobi.mw

Procedures
Calculation the equation of motions for Euclidean space and Minkowski space

"EQM := proc(eq, g,xup,xa,xu , eta ,var)"

Calling Sequence

 

EQM(eq, g, xup, xa, xu, eta, var)

Parameters

 

parameterSequence

-

eq, g, xup, xa, xu, eta, var

eq

out

equation of motion

g

out

metric

xup

out

velocitiy vector

xa

in

position vector

xu

in

vector of the independet coortinates

eta

in

signature matrix for Minkowski space

var

in

independet variable

 

``

 Procedur Code

 

restart; with(linalg); EQM := proc (eq, g, xup, xa, xu, eta, var) local J, Jp, xdd, l, xupp, ndim; ndim := vectdim(xu); xup := vector(ndim); xupp := vector(ndim); for l to ndim do xup[l] := diff(xu[l](var), var); xupp[l] := diff(diff(xu[l](var), var), var) end do; J := jacobian(xa, xu); g := multiply(transpose(J), eta, J); g := map(simplify, g); Jp := jacobian(multiply(J, xup), xu); Jp := map(simplify, Jp); xdd := multiply(inverse(g), transpose(J), eta, Jp, xup); xdd := map(simplify, xdd); xdd := map(convert, xdd, diff); eq := vector(vectdim(xupp)); for l to ndim do eq[l] := xupp[l]+xdd[l] = 0 end do end proc

``

Input

 

t := x[0]/c; xa := Vector(4, {(1) = t, (2) = r*cos(`ϕ`), (3) = r*sin(`ϕ`), (4) = x[3]}); xu := Vector(4, {(1) = x[0], (2) = r, (3) = `ϕ`, (4) = x[3]}); eta := Matrix(4, 4, {(1, 1) = -1, (1, 2) = 0, (1, 3) = 0, (1, 4) = 0, (2, 1) = 0, (2, 2) = 1, (2, 3) = 0, (2, 4) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = 1, (3, 4) = 0, (4, 1) = 0, (4, 2) = 0, (4, 3) = 0, (4, 4) = 1})

 

EQM(eq, g, xup, xa, xu, eta, tau):

Output EOM

 

for i to vectdim(xu) do eq[i] end do;

diff(diff(x[0](tau), tau), tau) = 0

 

diff(diff(r(tau), tau), tau)-(diff(`ϕ`(tau), tau))^2*r = 0

 

diff(diff(`ϕ`(tau), tau), tau)+2*(diff(`ϕ`(tau), tau))*(diff(r(tau), tau))/r = 0

 

diff(diff(x[3](tau), tau), tau) = 0

(5.1)

Output Line-Element

 

ds2 := expand(multiply(transpose(xup), g, xup));

-(diff(x[0](tau), tau))^2/c^2+(diff(r(tau), tau))^2+(diff(`ϕ`(tau), tau))^2*r^2+(diff(x[3](tau), tau))^2

(6.1)

Output Metric

 

assume(cos(`ϕ`)^2 = 1-sin(`ϕ`)^2); g := map(simplify, g)

array( 1 .. 4, 1 .. 4, [( 3, 3 ) = (r^2), ( 3, 4 ) = (0), ( 4, 1 ) = (0), ( 1, 1 ) = (-1/c^2), ( 4, 3 ) = (0), ( 4, 2 ) = (0), ( 2, 2 ) = (1), ( 3, 2 ) = (0), ( 3, 1 ) = (0), ( 2, 4 ) = (0), ( 1, 4 ) = (0), ( 1, 2 ) = (0), ( 2, 3 ) = (0), ( 4, 4 ) = (1), ( 2, 1 ) = (0), ( 1, 3 ) = (0)  ] )

(7.1)

``

``

 

Download bsp_jacobi_minkowski.mw

Hello,

I would like to determine the position jacobian matrix from a set of constraint equations.

Here my constraint equations :

eq1:=l1*cos(theta(t))+l2*sin(beta(t))-x(t)=0
eq2:=l1*sin(theta(t))-l2*cos(beta(t))=0

The jacobian matrix that I would like to determine is :

 

Can you help me to make a general procedure to calculate a jacobian position matrix from a set of constraint equations ?

Thank you for your help

 

I'm trying to find lypunov exponent for this  system of ODEs. I know I need to take the Jacobian but not sure if it's possible the way it's currently defined. If anyone could provide insight it would be much appreciated.

Eqns:= diff(omega(t),t)=-(G*MSat*beta^(2)*(xH(t)*sin(theta(t))-yH(t)* cos(theta(t)))*(xH(t)*cos(theta(t))+yH(t)*sin(theta(t))))/((xH(t)^(2)+yH(t)^(2))^(2.5)),diff(theta(t),t)=omega(t), diff(xH(t),t)=vxH(t),diff(vxH(t),t)=-(G*M*xH(t))/((xH(t)^(2)+yH(t)^(2))^(1.5)),diff(yH(t),t)=vyH(t),diff(vyH(t),t)=-(G*M*yH(t))/((xH(t)^(2)+yH(t)^(2))^(1.5)): ;

ICs := omega(0) = omega0, theta(0) = theta0, xH(0) = a*(1+e), yH(0) = 0, vxH(0) = 0, vyH(0) = sqrt(G*M*(1-e)/(a*(1+e)));

I want the exponent for omega, I  procedure that takes some initial conditions, changing just w0, and computes the long term value of omega. This plots a sort of bifurcation diagram. I'd like an estimate of the exponent to compare what I see. 

thanks for the help

ft

in Maple of a function with respect to a vector that contains product of variables?

I appologize before hand from my abuse of notation. 

Suppose I have a function

f:= x*y*z + 3*x^2*y*z;

and a vector defined as 

v:= <x*z | x^2*z>; #Column vector

and I would like to comput df/dv

So that the result from the Jacobian would be

J=<y , 3*y>; #Row vector

This is a simple example that can be solved by looking, but I would like to know if there is a way in maple to solve something like this. Some of the problems that I find are
1) The variables x*y*z commute i.e. x*y*z=z*x*y=y*z*x=x*z*y
2) And if I apply partial derivatives like  df/(dx*dz) = y+6*x*y and df/(dx*dx*dz) = 6 which is not the result that I desire, because what I want is the partial derivatives with respect to the functions x*z and x^2*z not to the variables x and z.

My application is far bigger than this example that I am posting, this is why I would like to find a way to produce this type of Jacobian. Any suggestion will be highly appreciate it.

For example, given a 3d point p(x,y,z), with (x,y,z) as its coordinates. Then it is transformed by rotation and translation, as 

p'=R(p)*p+t(p), where R(p) is a 3x3 rotation matrix that is a matrix of functions of p, and t(p) is a 3x1 vector function of p. 

My question is how to derive dp'/d(as a 3x3 matrix) using maple? 

To make it clear,I want to do it in a way that dp'/dp = ∂p'/∂p + ∂p'/∂R*∂R/∂p +  ∂p'/∂t*∂t/∂p

And I'd like to know each intermediate quantity, such as p'/∂R, R/∂p.


Anyone can help?

Thanks a lot. 

Determine using determinants the range of values of a (if any) such that
f(x,y,z)=4x^2+y^2+2z^2+2axy-4xz+2yz
has a minimum at (0,0,0).

From the theory, I understand that if the matrix corresponding to the coefficients of the function is positive definite, the function has a local min at the point. But, how do I get the range of values of a such that f is a min? Is this equivalent to finding a such that det(A) > 0?

 

2.

Now modify the function to also involve a parameter b: g(x,y,z)=bx^2+2axy+by^2+4xz-2a^2yz+2bz^2. We determine conditions on a and b such that g has a minimum at (0,0,0).
By plotting each determinant (using implicitplot perhaps, we can identify the region in the (a,b) plane where g has a local minimum.

Which region corresponds to a local minimum?

Now determine region(s) in the (a,b) plane where g has a local maximum.

I don't understand this part at all..


Hi there,
I have a set of differential equations whose solution, Jacobian matrix and its eigenvalues, direction field, phase portrait and nullclines, need to be computed.

Each of the equations has a varying parameter.

I know how to get the above for a single parameter value, but when I set a range of values for the parameters, Maple is not able to handle all cases as I would expect: solving the differential equation system:

eq1 := x*(1.6*(1-(1/100)*x)-phi*y)
eq2 := (x/(15+x)-0.3e-1*x-.4)*y+.6+theta
desys := [eq1, eq2];
vars := [x, y];
steadyStates := map2(eval, vars, [solve(desys)])

already yields an error:
Error, (in unknown) invalid input: Utilities:-SetEquations expects its 2nd argument, equations, to be of type set({boolean, algebraic, relation}), but received {-600*y+(Array(1..2, {(1) = 8400, (2) = 15900})), Array(1..5, {(1) = 0, (2) = 0, (3) = 0, (4) = 0, (5) = 0})}


The equations are the following:
de1 := diff(x(t), t) = x(t)*(1.6*(1-(1/100)*x(t))-phi*y(t));
de2 := diff(y(t), t) = (x(t)/(15+x(t))-0.3e-1*x(t)-.4)*y(t)+.6+theta

the parameters being:
phi:=[0 0.5 1 1.5 2]
theta:=[5. 10.]

How can I handle the situation so that Maple computes each of the above for each combination of the parameters?

I would like to avoid using two for loops and having to store all results in increasingly bigger and complicated arrays.

The worksheet at issue is this: MaplePrimes_Tumour_model_phi_theta_variation.mw


Thanks,
jon

Hi there,

I would like to have the Jacobian matrix of an ODE system evaluated, and their eigenvalues computed, at the steady states of the system.

I know how to get the Jacobian matrix evaluated and the eigenvalues computed on an individual basis, setting manually each steady state as the argument of the matrix.

However, I would like to have it in a loop, so that the loop manages all steady states, that is:

steadyStates:= solve(mySystem); # would yield a set of pairs/lists

for each steadyState

m:=Jacobian(steadyStateN); # evaluate the Jacobian matrix

ev:= eigenvals(m); # compute the eigenvalues and save them to another variable/array and print them

end for:

First, I am not to find a way to loop over my steadyStates.

Attached is an example where the Jacobian matrix and eigenvalues are computed individually, where the steady states have been hard-coded once they have been computed: MaplePrimes_Predator_prey_model_Jacobian.mw

 

Any ideas on how to do this?

Thanks,

jon

 

 

I have generated an 8x8 Jacobian, containing a few variables and several zeros as elements. I would like to translate this to LaTeX code. How can I first simplify what I have, to make it tractable?

Here is the Maple code:

eq_1 := Lambda-mu*S-(beta*(H+C+C1+C2)*S+tau*(T+C)*S)/(S+T+H+C+C1+C2+C1M+C2M);
eq_2 := (-beta*(H+C+C1+C2)*T+tau*(T+C)*S)/(S+T+H+C+C1+C2+C1M+C2M)-(mu+mu[T])*T;
eq_3 := (beta*(H+C+C1+C2)*S-tau*(T+C)*H)/(S+T+H+C+C1+C2+C1M+C2M)-(mu+mu[A])*H;
eq_4 := (beta*(H+C+C1+C2)*T+tau*(T+C)*H)/(S+T+H+C+C1+C2+C1M+C2M)-(mu+mu[T]+mu[A]+lambda[T])*C;
eq_5 := lambda[T]*C-(mu+mu[A]+rho[1]+eta[1])*C1;
eq_6 := rho[1]*C1-(mu+mu[A]+rho[2]+eta[2])*C2;
eq_7 := eta[1]*C1-(mu+rho[1]+gamma)*C1M;
eq_8 := eta[2]*C2+rho[1]*C1M-(mu+rho[2]+gamma*rho[1]/(rho[1]+rho[2]))*C2M;
J := VectorCalculus:-Jacobian([eq_1, eq_2, eq_3, eq_4, eq_5, eq_6, eq_7, eq_8], [S, T, H, C, C1, C2, C1M, C2M]);

JQDFE := eval(J, [S = Lambda/(beta-mu[A]), T = 0, H = Lambda*(beta/(mu+mu[A])-1)/(beta-mu[A]), C = 0, C1 = 0, C2 = 0, C1M = 0, C2M = 0]);

Thanks.

got an error when try to jacobian this

with(VectorCalculus):
f1 := -2*x1-x2; f2 := -x1-4*x2; g1 := 2*x1+3*x2-6; g2 := -x1; g3 := -x2;
penalty := lambda1*max(f1-M,0) + lambda2*max(f2-M,0) + (M^2)*(max(g1,0) + max(g2,0) + max(g3,0)):
obj := eval(penalty,[lambda1=3,lambda2=0.645,M=1]);
Hf := Jacobian(Jacobian(obj, [x1, x2, x3]), [x1, x2, x3]);

Error, invalid input: VectorCalculus:-Jacobian expects its 1st argument, f,
to be of type {Vector(algebraic), list(algebraic)}, but received 3*max(0, -2*x1-x2-1)+.645*max(0, -x1-4*x2-1)+max(0, 2*x1+3*x2-6)+max(0, -x1)+max(0, -x2)

 

Greetings,

       I am new to Maple and this forum. I would like to obtain a Jacobian of a system of 12 ODEs. What have I done wrongly with my code?

eq_1 := -B*a+A-V*(c+d+t+s+h)*a/(a+b+c+d+e+f+g+h+s+t+u+v)-W*(b+d)*a/(a+b+c+d+e+f+g+h+s+t+u+v);
eq_2 := W*(b+d)*a/(a+b+c+d+e+f+g+h+s+t+u+v)-V*(c+d+t+s+h)*b/(a+b+c+d+e+f+g+h+s+t+u+v)-(F*G+B+D)*b;
eq_3 := V*(c+d+t+s+h)*a/(a+b+c+d+e+f+g+h+s+t+u+v)-W*(b+d)*c/(a+b+c+d+e+f+g+h+s+t+u+v)-(B+E+C)*c;
eq_4 := V*(c+d+t+s+h)*b/(a+b+c+d+e+f+g+h+s+t+u+v)+W*(b+d)*c/(a+b+c+d+e+f+g+h+s+t+u+v)-(B+C+D+F)*d;
eq_5 := G*F*b-V*(c+d+t+s+h)*e/(a+b+c+d+e+f+g+h+s+t+u+v)-(B+H)*e;
eq_6 := H*e-V*(c+d+t+s+h)*f/(a+b+c+d+e+f+g+h+s+t+u+v)-(B+S)*f;
eq_7 := S*f-V*(c+d+t+s+h)*g/(a+b+c+d+e+f+g+h+s+t+u+v)-B*g;
eq_8 := V*(c+d+t+s+h)*g/(a+b+c+d+e+f+g+h+s+t+u+v)+S*s-(B+E+C)*h;
eq_9 := F*d+V*(c+d+t+s+h)*e/(a+b+c+d+e+f+g+h+s+t+u+v)-(B+C+H+T)*t;
eq_10 := H*t+V*(c+d+t+s+h)*f/(a+b+c+d+e+f+g+h+s+t+u+v)-(U+B+C+S+S)*s;
eq_11 := T*t+W*(b+d)*x/(a+b+c+d+e+f+g+h+s+t+u+v)-(B+H+Y)*u;
eq_12 := U*s-(B+S)*v+H*u-Y*H*v/(H+S);
with(linalg);
J := Jacobian([eq_1, eq_2, eq_3, eq_4, eq_5, eq_6, eq_7, eq_8, eq_9, eq_10, eq_11, eq_12], [a, b, c, d, e, f, g, h, s, t, u, v]);

I am getting the message: 

 Vector(4, {(1) = ` 12 x 12 `*Matrix, (2) = `Data Type: `*anything, (3) = `Storage: `*rectangular, (4) = `Order: `*Fortran_order})

Thanks!!

I have 2 problem with my jacobian matrix:

first: i can not evaluate 11*11 jacobian matrix. at last i can evaluate 10*10 matrix. can i solve this?
second: i want to export my matrix for matlab but i see this error : {export matrix"cannot convert matrix element to float[8] data type"}
so how i can use this matrix in my matlab code?
 my jacobian matrix:


with(VectorCalculus); Jacobian([VectorCalculus:-`+`(VectorCalculus:-`+`(VectorCalculus:-`+`(VectorCalculus:-`*`(2.68, ex), VectorCalculus:-`-`(VectorCalculus:-`*`(2, vx))), VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(3.500000001, e^VectorCalculus:-`*`(1.666666667, sqrt(VectorCalculus:-`+`(VectorCalculus:-`-`(VectorCalculus:-`+`(sqrt(VectorCalculus:-`+`(rx^2, ry^2)), sqrt(VectorCalculus:-`+`(VectorCalculus:-`+`(rx, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ex))))^2, VectorCalculus:-`+`(ry, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ey))))^2)))^2), VectorCalculus:-`-`(VectorCalculus:-`*`(4, vb^2)))))), VectorCalculus:-`+`(sqrt(VectorCalculus:-`+`(rx^2, ry^2)), sqrt(VectorCalculus:-`+`(VectorCalculus:-`+`(rx, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ex))))^2, VectorCalculus:-`+`(ry, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ey))))^2)))), VectorCalculus:-`+`(VectorCalculus:-`*`(rx, 1/sqrt(VectorCalculus:-`+`(rx^2, ry^2))), VectorCalculus:-`*`(1/2, VectorCalculus:-`*`(VectorCalculus:-`+`(VectorCalculus:-`*`(2, rx), VectorCalculus:-`-`(VectorCalculus:-`*`(4, vb(ex)))), 1/sqrt(VectorCalculus:-`+`(VectorCalculus:-`+`(rx, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ex))))^2, VectorCalculus:-`+`(ry, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ey))))^2)))))), ln(e)), 1/sqrt(VectorCalculus:-`+`(VectorCalculus:-`-`(VectorCalculus:-`+`(sqrt(VectorCalculus:-`+`(rx^2, ry^2)), sqrt(VectorCalculus:-`+`(VectorCalculus:-`+`(rx, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ex))))^2, VectorCalculus:-`+`(ry, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ey))))^2)))^2), VectorCalculus:-`-`(VectorCalculus:-`*`(4, vb^2)))))), VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(50.00000000, e^VectorCalculus:-`-`(VectorCalculus:-`*`(5.000000000, sqrt(VectorCalculus:-`+`(Rx^2, Ry^2))))), Rx), ln(e)), 1/sqrt(VectorCalculus:-`+`(Rx^2, Ry^2)))), VectorCalculus:-`+`(VectorCalculus:-`+`(VectorCalculus:-`+`(VectorCalculus:-`*`(2.68, ey), VectorCalculus:-`-`(VectorCalculus:-`*`(2, vy))), VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(3.500000001, e^VectorCalculus:-`*`(1.666666667, sqrt(VectorCalculus:-`+`(VectorCalculus:-`-`(VectorCalculus:-`+`(sqrt(VectorCalculus:-`+`(rx^2, ry^2)), sqrt(VectorCalculus:-`+`(VectorCalculus:-`+`(rx, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ex))))^2, VectorCalculus:-`+`(ry, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ey))))^2)))^2), VectorCalculus:-`-`(VectorCalculus:-`*`(4, vb^2)))))), VectorCalculus:-`+`(sqrt(VectorCalculus:-`+`(rx^2, ry^2)), sqrt(VectorCalculus:-`+`(VectorCalculus:-`+`(rx, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ex))))^2, VectorCalculus:-`+`(ry, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ey))))^2)))), VectorCalculus:-`+`(VectorCalculus:-`*`(ry, 1/sqrt(VectorCalculus:-`+`(rx^2, ry^2))), VectorCalculus:-`*`(1/2, VectorCalculus:-`*`(VectorCalculus:-`+`(VectorCalculus:-`*`(2, ry), VectorCalculus:-`-`(VectorCalculus:-`*`(4, vb(ey)))), 1/sqrt(VectorCalculus:-`+`(VectorCalculus:-`+`(rx, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ex))))^2, VectorCalculus:-`+`(ry, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ey))))^2)))))), ln(e)), 1/sqrt(VectorCalculus:-`+`(VectorCalculus:-`-`(VectorCalculus:-`+`(sqrt(VectorCalculus:-`+`(rx^2, ry^2)), sqrt(VectorCalculus:-`+`(VectorCalculus:-`+`(rx, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ex))))^2, VectorCalculus:-`+`(ry, VectorCalculus:-`-`(VectorCalculus:-`*`(2, vb(ey))))^2)))^2), VectorCalculus:-`-`(VectorCalculus:-`*`(4, vb^2)))))), VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(VectorCalculus:-`*`(50.00000000, e^VectorCalculus:-`-`(VectorCalculus:-`*`(5.000000000, sqrt(VectorCalculus:-`+`(Rx^2, Ry^2))))), Ry), ln(e)), 1/sqrt(VectorCalculus:-`+`(Rx^2, Ry^2)))), 1, 1, 1, 1, 1, 1, 1, 1, 1], [vx, vy, ex, ey, rx, ry, Ex, Ey, vb, Rx, Ry])

Hi all,

For research purposes, I have symbolic matrices of dimentions up to 100 x 50 (and above) with certain number of parameters. Then giving random value to those paramters, I want the numerical rank of the matrices.

 

Maple 17 does not seem to work well with symbolic ranks, where expentials are involved.

And more to it, both

Student[MultivariateCalculus][Jacobian]

and

VectorCalculus[Jacobian]

does not seem to work. ...

MAPLE Users,

Suppose I have a set of points in some N-dimensional space.  I would like to obtain a simple polynomial that

best fits the data.  I do not know in advance the form of the function, but a simple function that does not overfit

the data would probably be OK.  By "simple" I mean the smallest degree with or without cross-terms that gives a

decent fit.  My data set will typically be an external comma or tab separated text file.

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