MaplePrimes Questions

Hello!

I want to calculate Eigenvalues. Depending on values for digits and which datatype I choose Maple sometimes returns zero as Eigenvalues. Maybe there is a problem with the used routines: CLAPACK sw_dgeevx_, CLAPACK sw_zgeevx_.

Thank you for your suggestions!
 

``

 

Problems LinearAlgebra:-Eigenvalues, Digits, ':-datatype' = ':-sfloat', ':-datatype' = ':-complex'( ':-sfloat' )

 

restart;

interface( ':-displayprecision' = 5 ):
 

infolevel['LinearAlgebra'] := 5;
myPlatform := kernelopts( ':-platform' );
myVersion := kernelopts( ':-version' );

5

 

"windows"

 

`Maple 2018.2, X86 64 WINDOWS, Nov 16 2018, Build ID 1362973`

(1.1)

Example 1

 

A1 := Matrix( 5, 5, [[0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1], [-10201/1000, 30199/10000, -5049/250, 97/50, -48/5]] );

Matrix(5, 5, {(1, 1) = 0, (1, 2) = 1, (1, 3) = 0, (1, 4) = 0, (1, 5) = 0, (2, 1) = 0, (2, 2) = 0, (2, 3) = 1, (2, 4) = 0, (2, 5) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = 0, (3, 4) = 1, (3, 5) = 0, (4, 1) = 0, (4, 2) = 0, (4, 3) = 0, (4, 4) = 0, (4, 5) = 1, (5, 1) = -10201/1000, (5, 2) = 30199/10000, (5, 3) = -5049/250, (5, 4) = 97/50, (5, 5) = -48/5})

(1.1.1)

LinearAlgebra:-Eigenvalues( A1 );

CharacteristicPolynomial: working on determinant of minor 2
CharacteristicPolynomial: working on determinant of minor 3
CharacteristicPolynomial: working on determinant of minor 4
CharacteristicPolynomial: working on determinant of minor 5

 

Vector(5, {(1) = -10, (2) = 1/10+I, (3) = 1/10-I, (4) = 1/10+I, (5) = 1/10-I})

(1.1.2)

A11 := Matrix( op( 1, A1 ),( i,j ) -> evalf( A1[i,j] ), ':-datatype' = ':-sfloat' );

Matrix(5, 5, {(1, 1) = 0., (1, 2) = 1.00000, (1, 3) = 0., (1, 4) = 0., (1, 5) = 0., (2, 1) = 0., (2, 2) = 0., (2, 3) = 1.00000, (2, 4) = 0., (2, 5) = 0., (3, 1) = 0., (3, 2) = 0., (3, 3) = 0., (3, 4) = 1.00000, (3, 5) = 0., (4, 1) = 0., (4, 2) = 0., (4, 3) = 0., (4, 4) = 0., (4, 5) = 1.00000, (5, 1) = -10.20100, (5, 2) = 3.01990, (5, 3) = -20.19600, (5, 4) = 1.94000, (5, 5) = -9.60000})

(1.1.3)

Digits := 89;
LinearAlgebra:-Eigenvalues( A11 );

Digits := 89

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881249354686)

(1.1.4)

Digits := 90;
LinearAlgebra:-Eigenvalues( A11 );

Digits := 90

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881249352150)

(1.1.5)

A12 := Matrix( op( 1, A1 ),( i,j ) -> evalf( A1[i,j] ), ':-datatype' = ':-complex'( ':-sfloat' ) );

Matrix(5, 5, {(1, 1) = 0.+0.*I, (1, 2) = 1.00000+0.*I, (1, 3) = 0.+0.*I, (1, 4) = 0.+0.*I, (1, 5) = 0.+0.*I, (2, 1) = 0.+0.*I, (2, 2) = 0.+0.*I, (2, 3) = 1.00000+0.*I, (2, 4) = 0.+0.*I, (2, 5) = 0.+0.*I, (3, 1) = 0.+0.*I, (3, 2) = 0.+0.*I, (3, 3) = 0.+0.*I, (3, 4) = 1.00000+0.*I, (3, 5) = 0.+0.*I, (4, 1) = 0.+0.*I, (4, 2) = 0.+0.*I, (4, 3) = 0.+0.*I, (4, 4) = 0.+0.*I, (4, 5) = 1.00000+0.*I, (5, 1) = -10.20100+0.*I, (5, 2) = 3.01990+0.*I, (5, 3) = -20.19600+0.*I, (5, 4) = 1.94000+0.*I, (5, 5) = -9.60000+0.*I})

(1.1.6)

Digits := 100;
LinearAlgebra:-Eigenvalues( A12 );

Digits := 100

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881249345038)

(1.1.7)

Digits := 250;
LinearAlgebra:-Eigenvalues( A12 );

Digits := 250

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881342643606)

(1.1.8)

 

 

Example 2

 

A2 := Matrix(3, 3, [[0, 1, 0], [0, 0, 1], [3375, -675, 45]]);

Matrix(3, 3, {(1, 1) = 0, (1, 2) = 1, (1, 3) = 0, (2, 1) = 0, (2, 2) = 0, (2, 3) = 1, (3, 1) = 3375, (3, 2) = -675, (3, 3) = 45})

(1.2.1)

LinearAlgebra:-Eigenvalues( A2 );

IntegerCharacteristicPolynomial: Computing characteristic polynomial for a 3 x 3 matrix

IntegerCharacteristicPolynomial: Using prime 33554393
IntegerCharacteristicPolynomial: Using prime 33554383
IntegerCharacteristicPolynomial: Used total of  2  prime(s)

 

Vector(3, {(1) = 15, (2) = 15, (3) = 15})

(1.2.2)

A21 := Matrix( op( 1, A2 ),( i,j ) -> evalf( A2[i,j] ), ':-datatype' = ':-sfloat' );

Matrix(3, 3, {(1, 1) = 0., (1, 2) = 1.00000, (1, 3) = 0., (2, 1) = 0., (2, 2) = 0., (2, 3) = 1.00000, (3, 1) = 3375.00000, (3, 2) = -675.00000, (3, 3) = 45.00000})

(1.2.3)

Digits := 77;
LinearAlgebra:-Eigenvalues( A21 );

Digits := 77

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881342621686)

(1.2.4)

Digits := 78;
LinearAlgebra:-Eigenvalues( A21 );

Digits := 78

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881342617230)

(1.2.5)

A22 := Matrix( op( 1, A2 ),( i,j ) -> evalf( A2[i,j] ), ':-datatype' = ':-complex'( ':-sfloat' ) );

Matrix(3, 3, {(1, 1) = 0.+0.*I, (1, 2) = 1.00000+0.*I, (1, 3) = 0.+0.*I, (2, 1) = 0.+0.*I, (2, 2) = 0.+0.*I, (2, 3) = 1.00000+0.*I, (3, 1) = 3375.00000+0.*I, (3, 2) = -675.00000+0.*I, (3, 3) = 45.00000+0.*I})

(1.2.6)

Digits := 58;
LinearAlgebra:-Eigenvalues( A22 );

Digits := 58

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881342614934)

(1.2.7)

Digits := 59;
LinearAlgebra:-Eigenvalues( A22 );

Digits := 59

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881325525942)

(1.2.8)

 

 

Example 3

 

A3 := Matrix(4, 4, [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [-48841, 8840, -842, 40]]);

Matrix(4, 4, {(1, 1) = 0, (1, 2) = 1, (1, 3) = 0, (1, 4) = 0, (2, 1) = 0, (2, 2) = 0, (2, 3) = 1, (2, 4) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = 0, (3, 4) = 1, (4, 1) = -48841, (4, 2) = 8840, (4, 3) = -842, (4, 4) = 40})

(1.3.1)

LinearAlgebra:-Eigenvalues( A3 );

IntegerCharacteristicPolynomial: Computing characteristic polynomial for a 4 x 4 matrix
IntegerCharacteristicPolynomial: Using prime 33554393

IntegerCharacteristicPolynomial: Using prime 33554383
IntegerCharacteristicPolynomial: Used total of  2  prime(s)

 

Vector(4, {(1) = 10+11*I, (2) = 10-11*I, (3) = 10+11*I, (4) = 10-11*I})

(1.3.2)

A31 := Matrix( op( 1, A3 ),( i,j ) -> evalf( A3[i,j] ), ':-datatype' = ':-sfloat' );

Matrix(4, 4, {(1, 1) = 0., (1, 2) = 1.00000, (1, 3) = 0., (1, 4) = 0., (2, 1) = 0., (2, 2) = 0., (2, 3) = 1.00000, (2, 4) = 0., (3, 1) = 0., (3, 2) = 0., (3, 3) = 0., (3, 4) = 1.00000, (4, 1) = -48841.00000, (4, 2) = 8840.00000, (4, 3) = -842.00000, (4, 4) = 40.00000})

(1.3.3)

Digits := 75;
LinearAlgebra:-Eigenvalues( A31 );

Digits := 75

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881324662046)

(1.3.4)

Digits := 76;
LinearAlgebra:-Eigenvalues( A31 );

Digits := 76

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881324657710)

(1.3.5)

A32 := Matrix( op( 1, A3 ),( i,j ) -> evalf( A3[i,j] ), ':-datatype' = ':-complex'( ':-sfloat' ) );

Matrix(4, 4, {(1, 1) = 0.+0.*I, (1, 2) = 1.00000+0.*I, (1, 3) = 0.+0.*I, (1, 4) = 0.+0.*I, (2, 1) = 0.+0.*I, (2, 2) = 0.+0.*I, (2, 3) = 1.00000+0.*I, (2, 4) = 0.+0.*I, (3, 1) = 0.+0.*I, (3, 2) = 0.+0.*I, (3, 3) = 0.+0.*I, (3, 4) = 1.00000+0.*I, (4, 1) = -48841.00000+0.*I, (4, 2) = 8840.00000+0.*I, (4, 3) = -842.00000+0.*I, (4, 4) = 40.00000+0.*I})

(1.3.6)

Digits := 100;
LinearAlgebra:-Eigenvalues( A32 );

Digits := 100

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881324648198)

(1.3.7)

Digits := 250;
LinearAlgebra:-Eigenvalues( A32 );

Digits := 250

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881327288182)

(1.3.8)

 

 

Example 4

 

A4 := Matrix(8, 8, [[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1], [-1050625/20736, 529925/1296, -15417673/10368, 3622249/1296, -55468465/20736, 93265/108, -1345/8, 52/3]]);

Matrix(8, 8, {(1, 1) = 0, (1, 2) = 1, (1, 3) = 0, (1, 4) = 0, (1, 5) = 0, (1, 6) = 0, (1, 7) = 0, (1, 8) = 0, (2, 1) = 0, (2, 2) = 0, (2, 3) = 1, (2, 4) = 0, (2, 5) = 0, (2, 6) = 0, (2, 7) = 0, (2, 8) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = 0, (3, 4) = 1, (3, 5) = 0, (3, 6) = 0, (3, 7) = 0, (3, 8) = 0, (4, 1) = 0, (4, 2) = 0, (4, 3) = 0, (4, 4) = 0, (4, 5) = 1, (4, 6) = 0, (4, 7) = 0, (4, 8) = 0, (5, 1) = 0, (5, 2) = 0, (5, 3) = 0, (5, 4) = 0, (5, 5) = 0, (5, 6) = 1, (5, 7) = 0, (5, 8) = 0, (6, 1) = 0, (6, 2) = 0, (6, 3) = 0, (6, 4) = 0, (6, 5) = 0, (6, 6) = 0, (6, 7) = 1, (6, 8) = 0, (7, 1) = 0, (7, 2) = 0, (7, 3) = 0, (7, 4) = 0, (7, 5) = 0, (7, 6) = 0, (7, 7) = 0, (7, 8) = 1, (8, 1) = -1050625/20736, (8, 2) = 529925/1296, (8, 3) = -15417673/10368, (8, 4) = 3622249/1296, (8, 5) = -55468465/20736, (8, 6) = 93265/108, (8, 7) = -1345/8, (8, 8) = 52/3})

(1.4.1)

LinearAlgebra:-Eigenvalues( A4 );

CharacteristicPolynomial: working on determinant of minor 2
CharacteristicPolynomial: working on determinant of minor 3

CharacteristicPolynomial: working on determinant of minor 4
CharacteristicPolynomial: working on determinant of minor 5
CharacteristicPolynomial: working on determinant of minor 6
CharacteristicPolynomial: working on determinant of minor 7
CharacteristicPolynomial: working on determinant of minor 8

 

Vector(8, {(1) = 1/3-(1/4)*I, (2) = 1/3+(1/4)*I, (3) = 4-5*I, (4) = 4+5*I, (5) = 1/3-(1/4)*I, (6) = 1/3+(1/4)*I, (7) = 4-5*I, (8) = 4+5*I})

(1.4.2)

A41 := Matrix( op( 1, A4 ),( i,j ) -> evalf( A4[i,j] ), ':-datatype' = ':-sfloat' );

Matrix(8, 8, {(1, 1) = 0., (1, 2) = 1.00000, (1, 3) = 0., (1, 4) = 0., (1, 5) = 0., (1, 6) = 0., (1, 7) = 0., (1, 8) = 0., (2, 1) = 0., (2, 2) = 0., (2, 3) = 1.00000, (2, 4) = 0., (2, 5) = 0., (2, 6) = 0., (2, 7) = 0., (2, 8) = 0., (3, 1) = 0., (3, 2) = 0., (3, 3) = 0., (3, 4) = 1.00000, (3, 5) = 0., (3, 6) = 0., (3, 7) = 0., (3, 8) = 0., (4, 1) = 0., (4, 2) = 0., (4, 3) = 0., (4, 4) = 0., (4, 5) = 1.00000, (4, 6) = 0., (4, 7) = 0., (4, 8) = 0., (5, 1) = 0., (5, 2) = 0., (5, 3) = 0., (5, 4) = 0., (5, 5) = 0., (5, 6) = 1.00000, (5, 7) = 0., (5, 8) = 0., (6, 1) = 0., (6, 2) = 0., (6, 3) = 0., (6, 4) = 0., (6, 5) = 0., (6, 6) = 0., (6, 7) = 1.00000, (6, 8) = 0., (7, 1) = 0., (7, 2) = 0., (7, 3) = 0., (7, 4) = 0., (7, 5) = 0., (7, 6) = 0., (7, 7) = 0., (7, 8) = 1.00000, (8, 1) = -50.66671, (8, 2) = 408.89275, (8, 3) = -1487.04408, (8, 4) = 2794.94522, (8, 5) = -2674.98384, (8, 6) = 863.56481, (8, 7) = -168.12500, (8, 8) = 17.33333})

(1.4.3)

Digits := 74;
LinearAlgebra:-Eigenvalues( A41 );

Digits := 74

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881317242630)

(1.4.4)

Digits := 75;
LinearAlgebra:-Eigenvalues( A41 );

Digits := 75

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_dgeevx_

 

Vector[column](%id = 18446745881317239134)

(1.4.5)

A42 := Matrix( op( 1, A4 ),( i,j ) -> evalf( A4[i,j] ), ':-datatype' = ':-complex'( ':-sfloat' ) );

Matrix(8, 8, {(1, 1) = 0.+0.*I, (1, 2) = 1.00000+0.*I, (1, 3) = 0.+0.*I, (1, 4) = 0.+0.*I, (1, 5) = 0.+0.*I, (1, 6) = 0.+0.*I, (1, 7) = 0.+0.*I, (1, 8) = 0.+0.*I, (2, 1) = 0.+0.*I, (2, 2) = 0.+0.*I, (2, 3) = 1.00000+0.*I, (2, 4) = 0.+0.*I, (2, 5) = 0.+0.*I, (2, 6) = 0.+0.*I, (2, 7) = 0.+0.*I, (2, 8) = 0.+0.*I, (3, 1) = 0.+0.*I, (3, 2) = 0.+0.*I, (3, 3) = 0.+0.*I, (3, 4) = 1.00000+0.*I, (3, 5) = 0.+0.*I, (3, 6) = 0.+0.*I, (3, 7) = 0.+0.*I, (3, 8) = 0.+0.*I, (4, 1) = 0.+0.*I, (4, 2) = 0.+0.*I, (4, 3) = 0.+0.*I, (4, 4) = 0.+0.*I, (4, 5) = 1.00000+0.*I, (4, 6) = 0.+0.*I, (4, 7) = 0.+0.*I, (4, 8) = 0.+0.*I, (5, 1) = 0.+0.*I, (5, 2) = 0.+0.*I, (5, 3) = 0.+0.*I, (5, 4) = 0.+0.*I, (5, 5) = 0.+0.*I, (5, 6) = 1.00000+0.*I, (5, 7) = 0.+0.*I, (5, 8) = 0.+0.*I, (6, 1) = 0.+0.*I, (6, 2) = 0.+0.*I, (6, 3) = 0.+0.*I, (6, 4) = 0.+0.*I, (6, 5) = 0.+0.*I, (6, 6) = 0.+0.*I, (6, 7) = 1.00000+0.*I, (6, 8) = 0.+0.*I, (7, 1) = 0.+0.*I, (7, 2) = 0.+0.*I, (7, 3) = 0.+0.*I, (7, 4) = 0.+0.*I, (7, 5) = 0.+0.*I, (7, 6) = 0.+0.*I, (7, 7) = 0.+0.*I, (7, 8) = 1.00000+0.*I, (8, 1) = -50.66671+0.*I, (8, 2) = 408.89275+0.*I, (8, 3) = -1487.04408+0.*I, (8, 4) = 2794.94522+0.*I, (8, 5) = -2674.98384+0.*I, (8, 6) = 863.56481+0.*I, (8, 7) = -168.12500+0.*I, (8, 8) = 17.33333+0.*I})

(1.4.6)

Digits := 100;
LinearAlgebra:-Eigenvalues( A42 );

Digits := 100

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881317227806)

(1.4.7)

Digits := 250;
LinearAlgebra:-Eigenvalues( A42 );

Digits := 250

 

Eigenvalues: calling external function
Eigenvalues: initializing the output object
Eigenvalues: using software external library
Eigenvalues: CLAPACK sw_zgeevx_

 

Vector[column](%id = 18446745881356880102)

(1.4.8)

 

 

 

 

 

 

 

 

 

 

``


 

Download Problems_LinearAlgebra_Eigenvalues.mw

has anyone tried to use Peter Fritzone's book with maplesim helloworld modelica??

 

The simple approach from Peter (creating a custom modelica component) is to getting started by running a simple simulation where x_dot(t) = - a*x,  where the normal form is x(t) = x^-a for logarithmic type decay.

plot of x(t) should be a decay curve.  

 

Maple Code

A:-LinkModel = fine, connects

 

SimData := A:-Simulate(outputs = x, returntype = datapoints, tf = 2);

Error, invalid input: Simulate expects value for keyword parameter [outputs, output] to be of type {list(algebraic), list(anyfunc(identical(t)))}, but received x
 

***********

Simcode

 

Model Main;

Imports

public HelloWorld HelloWorld1 annotation(Placement(transformation(origin={100.0,200.0},extent={{-20.0,-20.0},{20.0,20.0}},rotation=0)));
    annotation(
            Diagram(coordinateSystem(preserveAspectRatio=true, extent={{0,0},{200.0,200.0}}),graphics),
            Icon(coordinateSystem(preserveAspectRatio=true, extent={{0,0},{200.0,200.0}}),graphics={Rectangle(extent={{0,0},{200.0,200.0}}, lineColor={0,0,0})}),
            uses(Modelica(version="3.2.3")),
            experiment(
              StartTime = 0,
              StopTime = 2.0,
              __Maplesoft_solver = "ck45",
              __Maplesoft_adaptive = true,
              Tolerance = 0.1e-4,
              __Maplesoft_tolerance_abs = 0.1e-4,
              __Maplesoft_step_size = 0.1e-2,
              __Maplesoft_min_step_size = 0,
              __Maplesoft_max_step_size = 0,
              __Maplesoft_plot_points = 2000,
              __Maplesoft_numeric_jacobian = false,
              __Maplesoft_constraint_iterations = 50,
              __Maplesoft_event_iterations = 100,
              __Maplesoft_algebraic_error_control = false,
              __Maplesoft_algebraic_error_relaxation_factor = 1,
              __Maplesoft_rate_hysteresis = 0.1e-9,
              __Maplesoft_reduce_events = false,
              __Maplesoft_integration_diagnostics = false,
              __Maplesoft_compiler = true,
              __Maplesoft_compiler_optimize = true,
              __Maplesoft_scale_method = "none",
              __Maplesoft_plot_event_points = true
            )
    );

 

end Main;

class HelloWorld
    Real x (start =1);
    parameter Real a = 1;
equation
    der(x) = -a*x;
end HelloWorld;

 

Dear Maple users,

I want to find an expression for the Fourier Transform (FT) of an expression like  f(t)=exp(-t^2)/t^a, where a>0 is a constant.

I note that integer values of a (postive or negative) is ok; but non-integer fails. See sheet attached where I have tried 1 or 2 cases, a=0, 1, 0.3, etc.

So the questions are:

(1) how can I find the FT of the above for typical non-integer values of a>0 ?

(2) how can I find the FT of the above for general a -- i.e. declare a as a parameter?

 

Thanks

Nadeem


 

with(inttrans)

fourier(exp(-t^2), t, w)

exp(-(1/4)*w^2)*Pi^(1/2)

(1)

fourier(t*exp(-t^2), t, w)

-((1/2)*I)*w*exp(-(1/4)*w^2)*Pi^(1/2)

(2)

fourier(exp(-t^2)/t, t, w)

-I*Pi*erf((1/2)*w)

(3)

fourier(t^.3*exp(-t^2), t, w)

fourier(t^(3/10)*exp(-t^2), t, w)

(4)

fourier(exp(-t^2)/t^.3, t, w)

fourier(exp(-t^2)/t^(3/10), t, w)

(5)

``


 

Download Sheet_fourier_1.mw

hi 

i want to plot this equations , i want to show that step by step and remind previous . i plot with animte plot but it do not show the previous

 

restart;
with(plottools);
co := blue;
with(plots);

t := 1;
for i from 20 by -1 to 0 do t := t+1; a[i] := -i*x/t+i; p[i] := plot(a[i], x = 0 .. 20, y = 0 .. 20, color = co, thickness = 3) end do;

plots[animate](plot, [a[k], x = 0 .. 20, y = 0 .. 20], k = [seq(i, i = 1 .. 20)]);

pde := (diff(u(r, theta), r) + r * diff(u(r, theta), r, r) + diff(u(r, theta), theta, theta) / r ) / r:
iv := u( 1, theta) = 0, u( 3, theta) = theta, u( r, 0) = 10, u( r, Pi/2) = 0:
           Maple 2019 returns a symbolic solution for PDE:
pdsolve([pde, iv], u(r, theta));
   But for the numeric option, it returns a message saying that Maple is unable to handle elliptical PDEs.
pdsolve(pde, {iv}, numeric, time = t, range = 1 .. 3);

Error, (in pdsolve/numeric) unable to handle elliptic PDEs
I found it strange.

Oliveira.

How to get the functional form of interpolation in the given example below

 

GP.mw

Hi

please help me, how could I write this? 

Hi, 

The procedure Statistics:-ChiSquareSuitableModelTest returns wrong or stupid results in some situations.
The stupid answer can easily be avoided if the user is careful enough.
The wrong answer is more serious: the standard deviation (in the second case below) is not correctly estimated.

PS: the expression "CORRECT ANSWER" is a short for "POTENTIALLY CORRECT ANSWER" given that what ChiSquareSuitableModelTest really does is not documented
 

restart:

with(Statistics):

randomize():

N := 100:
S := Sample(Normal(0, 1), N):

infolevel[Statistics] := 1:

# 0 parameter to fit from the sample S  CORRECT ANSWER

ChiSquareSuitableModelTest(S, Normal(0, 1), level = 0.5e-1):
print():

Chi-Square Test for Suitable Probability Model
----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Bins:                    10
Degrees of freedom:      9
Distribution:            ChiSquare(9)
Computed statistic:      15.8
Computed pvalue:         0.0711774
Critical value:          16.9189774487099
Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false

 

(1)

# 2 parameters (mean and standard deviation) to fit from the sample S  INCORRECT ANSWER

ChiSquareSuitableModelTest(S, Normal(a, b), level = 0.5e-1, fittedparameters = 2):


print():
# verification
m := Mean(S);
s := StandardDeviation(S);
t := sqrt(add((S-~m)^~2) / (N-1));

print():
error "the estimation of the StandardDeviation ChiSquareSuitableModelTest is not correct";
print():

Chi-Square Test for Suitable Probability Model

----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Model specialization:    [a = -.2143e-1, b = .8489]
Bins:                    10
Degrees of freedom:      7
Distribution:            ChiSquare(7)
Computed statistic:      3.8
Computed pvalue:         0.802504
Critical value:          14.0671405764057
Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false

 

 

HFloat(-0.021425681632689854)

 

HFloat(0.8531979363682092)

 

HFloat(0.8531979363682094)

 

 

Error, the estimation of the StandardDeviation ChiSquareSuitableModelTest is not correct

 

(2)

# ONLY 1 parameter (mean OR standard deviation ?) to fit from the sample S  STUPID ANSWER
#
# A stupid answer: the parameter to fit not being declared, the procedure should return
# an error of the type "don(t know what is the paramater tio fit"
ChiSquareSuitableModelTest(S, Normal(a, b), level = 0.5e-1, fittedparameters = 1):


print():
WARNING("ChiSquareSuitableModelTest should return it can't fit a single parameter");
print():

Chi-Square Test for Suitable Probability Model

----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Model specialization:    [a = -.2143e-1, b = .8489]
Bins:                    10
Degrees of freedom:      8
Distribution:            ChiSquare(8)
Computed statistic:      3.8
Computed pvalue:         0.874702
Critical value:          15.5073130558655
Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false

 

 

Warning, ChiSquareSuitableModelTest should return it can't fit a single parameter

 

(3)

ChiSquareSuitableModelTest(S, Normal(a, 1), level = 0.5e-1, fittedparameters = 1):  #CORRECT ANSWER
print():

# verification
m := Mean(S);
print():

Chi-Square Test for Suitable Probability Model

----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Model specialization:    [a = -.2143e-1]
Bins:                    10
Degrees of freedom:      8
Distribution:            ChiSquare(8)
Computed statistic:      16.4
Computed pvalue:         0.0369999
Critical value:          15.5073130558655
Result: [Rejected]
This statistical test provides evidence that the null hypothesis is false

 

 

HFloat(-0.021425681632689854)

 

(4)

ChiSquareSuitableModelTest(S, Normal(0, b), level = 0.5e-1, fittedparameters = 1):  #CORRECT ANSWER

print():
# verification
s := sqrt((add(S^~2) - 0^2) / N);
print():

Chi-Square Test for Suitable Probability Model

----------------------------------------------
Null Hypothesis:
Sample was drawn from specified probability distribution
Alt. Hypothesis:
Sample was not drawn from specified probability distribution
Model specialization:    [b = .8492]
Bins:                    10
Degrees of freedom:      8
Distribution:            ChiSquare(8)
Computed statistic:      6.4
Computed pvalue:         0.60252
Critical value:          15.5073130558655
Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false

 

 

HFloat(0.8491915633531496)

 

(5)

 


 

Download ChiSquareSuitableModelTest.mw

How to get the result of this limit? I don't get the result.
 

limit(sum(1/(i*sqrt(i+1)+(i+1)*sqrt(i)), i = 1 .. n), n = infinity);

With Mathematica, I got the output is 1.

Hello. Let's say I have a list of many items. Well, let's list A:=[1,1.732,1.23,4.42,9,6.45,3.45,8.428,9.1,12]. How to get three numbers from it randomly?

In answers given in 

In https://www.mapleprimes.com/questions/227546-How-To-Make-Odetest-Verify-Dsolve

It shows that odetest() did not verify a solution to ODE becuase solution was using hypergeom special functions. If the solution to the ODE was in integral form, then odetest() will verify it OK.

But what to do if the solution I want to verify is already in hypergoem? If I try odetest() it will fail to verify now. Then I can try to convert the solution to integral form and try again.

But when  using convert(sol,Int) followed by odetest() it did not work.

The solutions I try to verify are hand solutions or book solutions, and not coming from dsolve. 

But some of them are the same solution that comes from dsolve() when not using the useInt option. 

Also, I am doing this all inside a Maple program. It is not an interactive process. So I can't do plots and look at them to decide on anything. So verification must all be implemented in code.

The question is: Why did convert(hand_solution,Int) not give the same result as dsolve(ode,useInt)? Is there another way around this? (May be I am asking for too much in this one based on answers in the above link, So that is OK if not possible. But I really like the solution given when using "useInt" option. Much more clear than otherwise).
 

restart;

ode := diff(y(x), x)*(x^3 + 1)^(2/3) + (1 + y(x)^3)^(2/3) = 0;
sol_int:=dsolve(ode,useInt);
odetest(sol_int,ode); #OK now, since solution in integral form

(diff(y(x), x))*(x^3+1)^(2/3)+(1+y(x)^3)^(2/3) = 0

Int(1/(x^3+1)^(2/3), x)+Intat(1/(_a^3+1)^(2/3), _a = y(x))+_C1 = 0

0

hand_solution:= x*hypergeom([1/3, 2/3], [4/3], -x^3) + y(x)*hypergeom([1/3, 2/3], [4/3], -y(x)^3) + _C1 = 0;
convert(hand_solution,Int); #Why this did not give same result as ABOVE?

x*hypergeom([1/3, 2/3], [4/3], -x^3)+y(x)*hypergeom([1/3, 2/3], [4/3], -y(x)^3)+_C1 = 0

(2/9)*x*Pi*3^(1/2)*(Int(1/(_t1^(1/3)*(1-_t1)^(1/3)*(x^3*_t1+1)^(1/3)), _t1 = 0 .. 1))/GAMMA(2/3)^3+(2/9)*y(x)*Pi*3^(1/2)*(Int(1/(_t1^(1/3)*(1-_t1)^(1/3)*(y(x)^3*_t1+1)^(1/3)), _t1 = 0 .. 1))/GAMMA(2/3)^3+_C1 = 0

odetest(%,ode); #does not give zero

-y(x)^3*(1+y(x)^3)^(2/3)*(Int(_t1^(2/3)/((1-_t1)^(1/3)*(y(x)^3*_t1+1)^(4/3)), _t1 = 0 .. 1))+(x^3+1)^(2/3)*(Int(_t1^(2/3)/((1-_t1)^(1/3)*(x^3*_t1+1)^(4/3)), _t1 = 0 .. 1))*x^3-(x^3+1)^(2/3)*(Int(1/(_t1^(1/3)*(1-_t1)^(1/3)*(x^3*_t1+1)^(1/3)), _t1 = 0 .. 1))+(1+y(x)^3)^(2/3)*(Int(1/(_t1^(1/3)*(1-_t1)^(1/3)*(y(x)^3*_t1+1)^(1/3)), _t1 = 0 .. 1))

 

 

Maple 2019.1

Download 072619_2.mw

 

 

 

It seems that the answer to my question is deleted!!!

So, I again repeat it.

How I can calculate this integral?

I want to calculate integral with the constants Aj]j=1,2.

The Amount of these constants  unknown this stage.

Thanks

INTEGRAL

integral.mw

I am trying to evaluate any which way the integral:

int(exp(-(sqrt(4*x^2+4*y^2+4*z^2)^3)), z = -sqrt(4-x^2-y^2).. sqrt(4-x^2-y^2),y=-sqrt(4-x^2)..sqrt(4-x^2),x=-2..2);

The program just hangs, so i click on 'stop current operation'.

Then I tried:

evalf(Int(exp(-(sqrt(4*x^2+4*y^2+4*z^2)^3)), z = -sqrt(4-x^2-y^2).. sqrt(4-x^2-y^2),y=-sqrt(4-x^2)..sqrt(4-x^2),x=-2..2));

It returns the integral back unevaluated.

It's true that I could use a change of variables, changing to spherical coordinates would be best here. But I would like to know if I did set up the original Cartesian integral correctly. Is there some workaround I can apply to get a numeric answer. I am satisfied with a decimal approximation. Then I can compare to the change of variable result. An exact answer would be even better of course.

THis is another ode which I am not able to get odetest to give zero. Any one knows of a trick to verify this solution? It might be just that the solution is too complicated for odetest to verify?


 

restart;

ode:=diff(y(x),x)*(x^3+1)^(2/3)+(1+y(x)^3)^(2/3) = 0;
sol:=dsolve(ode);

(diff(y(x), x))*(x^3+1)^(2/3)+(1+y(x)^3)^(2/3) = 0

x*hypergeom([1/3, 2/3], [4/3], -x^3)+y(x)*hypergeom([1/3, 2/3], [4/3], -y(x)^3)+_C1 = 0

odetest(sol,ode);

-9*(1+y(x)^3)^(1/3)*(x^3+1)^(2/3)*hypergeom([4/3, 5/3], [7/3], -x^3)*x^3*GAMMA(2/3)*(-y(x)^3)^(1/6)/(9*hypergeom([4/3, 5/3], [7/3], -y(x)^3)*y(x)^3*(-y(x)^3)^(1/6)*(1+y(x)^3)^(1/3)*GAMMA(2/3)-4*Pi*3^(1/2)*LegendreP(-1/3, -1/3, -y(x)^3/(1+y(x)^3)+1/(1+y(x)^3)))+9*y(x)^6*hypergeom([4/3, 5/3], [7/3], -y(x)^3)*GAMMA(2/3)*(-y(x)^3)^(1/6)/(9*hypergeom([4/3, 5/3], [7/3], -y(x)^3)*y(x)^3*(-y(x)^3)^(1/6)*(1+y(x)^3)^(1/3)*GAMMA(2/3)-4*Pi*3^(1/2)*LegendreP(-1/3, -1/3, -y(x)^3/(1+y(x)^3)+1/(1+y(x)^3)))+9*y(x)^3*hypergeom([4/3, 5/3], [7/3], -y(x)^3)*GAMMA(2/3)*(-y(x)^3)^(1/6)/(9*hypergeom([4/3, 5/3], [7/3], -y(x)^3)*y(x)^3*(-y(x)^3)^(1/6)*(1+y(x)^3)^(1/3)*GAMMA(2/3)-4*Pi*3^(1/2)*LegendreP(-1/3, -1/3, -y(x)^3/(1+y(x)^3)+1/(1+y(x)^3)))-4*(1+y(x)^3)^(2/3)*Pi*3^(1/2)*LegendreP(-1/3, -1/3, -(y(x)^3-1)/(1+y(x)^3))/(9*hypergeom([4/3, 5/3], [7/3], -y(x)^3)*y(x)^3*(-y(x)^3)^(1/6)*(1+y(x)^3)^(1/3)*GAMMA(2/3)-4*Pi*3^(1/2)*LegendreP(-1/3, -1/3, -y(x)^3/(1+y(x)^3)+1/(1+y(x)^3)))+4*(1+y(x)^3)^(1/3)*(x^3+1)^(1/3)*Pi*3^(1/2)*LegendreP(-1/3, -1/3, -(x^3-1)/(x^3+1))*(-y(x)^3)^(1/6)/((-x^3)^(1/6)*(9*hypergeom([4/3, 5/3], [7/3], -y(x)^3)*y(x)^3*(-y(x)^3)^(1/6)*(1+y(x)^3)^(1/3)*GAMMA(2/3)-4*Pi*3^(1/2)*LegendreP(-1/3, -1/3, -y(x)^3/(1+y(x)^3)+1/(1+y(x)^3))))

simplify(%);

-9*((4/9)*(1+y(x)^3)^(2/3)*Pi*3^(1/2)*LegendreP(-1/3, -1/3, (-y(x)^3+1)/(1+y(x)^3))*(-x^3)^(1/6)+(-(4/9)*(1+y(x)^3)^(1/3)*(x^3+1)^(1/3)*Pi*3^(1/2)*LegendreP(-1/3, -1/3, (-x^3+1)/(x^3+1))+(-x^3)^(1/6)*((-y(x)^6-y(x)^3)*hypergeom([4/3, 5/3], [7/3], -y(x)^3)+x^3*(1+y(x)^3)^(1/3)*hypergeom([4/3, 5/3], [7/3], -x^3)*(x^3+1)^(2/3))*GAMMA(2/3))*(-y(x)^3)^(1/6))/((-x^3)^(1/6)*(9*hypergeom([4/3, 5/3], [7/3], -y(x)^3)*y(x)^3*(-y(x)^3)^(1/6)*(1+y(x)^3)^(1/3)*GAMMA(2/3)-4*Pi*3^(1/2)*LegendreP(-1/3, -1/3, (-y(x)^3+1)/(1+y(x)^3))))

 


 

Download 072619.mw

Maple 2019.1, Physics 395

Download 072619.mw

 

 

This must be a simple question. I have a simple expression:

test := -2+exp(theta)+exp(-theta)

and want to get that factored as a polynomial in the variable exp(-theta).

That is of course easy by hand, but the same problem appears frequently, as a part in further simplifications. There must be an easy way to do it, but I cannot find it. 

            


 

 

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