Maple Questions and Posts

These are Posts and Questions associated with the product, Maple

In the positive range Maple confirms that this is true.
In the real range Maple fails to provide an answer (see attachments).

Is this identity correct?

restart

kernelopts(version)

`Maple 2022.0, X86 64 WINDOWS, Mar 8 2022, Build ID 1599809`

(1)

NULL

is(arctan(-x) = -arctan(x))

true

(2)

`assuming`([is(arctan(x, y) = -arctan(-x, y))], [x::real, y::real])

FAIL

(3)

`assuming`([is(arctan(x, y) = -arctan(-x, y))], [x::positive, y::positive])

true

(4)

`assuming`([simplify(arctan(-x, y)+arctan(x, y), trig)], [x::real, y::real])

arctan(-x, y)+arctan(x, y)

(5)

But

plot3d([arctan(-x, y)+arctan(x, y)], x = -1000000 .. 1000000, y = -1000000 .. 1000000, title = arctan(-x, y)+arctan(x, y))

 

On a unit circle

x = cos(alpha), y = sin(alpha)

x = cos(alpha), y = sin(alpha)

(6)

subs(x = cos(alpha), y = sin(alpha), arctan(-x, y)+arctan(x, y))

arctan(-cos(alpha), sin(alpha))+arctan(cos(alpha), sin(alpha))

(7)

`assuming`([simplify(%)], [alpha::real])

arctan(-cos(alpha), sin(alpha))+arctan(cos(alpha), sin(alpha))

(8)

`assuming`([simplify(%)], [alpha::positive])

arctan(-cos(alpha), sin(alpha))+arctan(cos(alpha), sin(alpha))

(9)

`assuming`([simplify(%)], [-Pi < alpha and alpha < Pi])

arctan(-cos(alpha), sin(alpha))+arctan(cos(alpha), sin(alpha))

(10)

plot(arctan(-cos(alpha), sin(alpha))+arctan(cos(alpha), sin(alpha)), alpha = -2*Pi .. 2*Pi, axes = boxed, color = red)

 

NULL


Download arctan_xy_simplify.mw

and another maybe related case where simplification does not work

arctan_xy_simplify_2.mw

Hello everyone,

I am trying to solve an equation using the "solve(...)" function but the coefficients of the equation are changing.

I am using the coefficients as list but the "solve(...)" function gives error: "Error, (in assuming) when calling 'Engine:-Dispatch'. Received: 'badly formed input to solve: not fully algebraic'"

I am attaching the Maple document.

 

However, what I want to achieve, is the solution for each value in the list.

Here is an easy example of what I am saying:

a=3*x+k where k=[list of values], a=[another list of values].

I want to find "x" for each pair of values (a,k) in the list.

 

Thank you !
 

``

``

LCC Resonant Converter - Frequency Finder

 

 

restart``

with(Student[Calculus1])

V__out := 50.0

`&omega;__line` := 2.0*Pi*50

t := [seq(0 .. 10*10^(-3), .1*10^(-3))]

theta := `&omega;__line`*t

v__line := 85*sqrt(2.0)*`~`[sin](theta)

m := 1

Q__s := 3.2*`~`[`^`](`~`[sin](theta), 2)

``

f__n := `assuming`([solve((1/120)*V__out = 1/sqrt((-m*x^2+m+1)^2+(Q__s*(x-1/x))^2), x, useassumptions)], [x::positive])

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

 

``


 

Download LCC_gain_freqFinder.mwLCC_gain_freqFinder.mw

ds := DerivedSeries(PermutationGroup({[[2, 3, 5, 4]], [[1, 2, 3, 4, 5]]}))

will get a series group like:

How to get the group by the red line point out? Then I can use GroupOrder to get the order of it. I note GroupOrder[ds[2]] don't work

Hello, i want to do convert numbers to binary code 8 bytes and I want to sum the digits of each number 

For example first number is 0 i want to write 0 0 0 0 0 0 0 0 , sum=0, for 1= 0 0 0 0 0 0 0 1,  sum =1 for 7= 0 0 0 0 0 1 1 1 sum=3 etc.....
my code is this;

for i from 0 to 10 do;
> S(i):=convert(i,binary);
> end do;

and output is 

                              S(0) := 0
                              S(1) := 1
                              S(2) := 10
                            S(3) := 11
                            S(4):=100
                             S(5) := 101
                             S(6) := 110
                            S(7) := 111
                           S(8) := 1000
                             S(9) := 1001
                            S(10) := 1010
                and  Histogram(S(i));  doesnt work.                

I want to reverse a catenated expression (eg. xxtt ->x,x,t,t). I don't know the command to achieve this. Anyone with an idea please help

Thanks

Hey I’m trying to define a variable as the solution to a second order nonhomogeneous differential equation with an initial value problem.

I have tried the method below, but it doesn’t work.

N__1 := dsolve([diff(y(x), x, x) + 3*diff(y(x), x) + 2*y(x) = x^2 + 5, eval(y(x), x = 0) = 1, eval(diff(y(x), x), x = 0) = 1], y(x))

It kind of works it gives me the expression below

N__1 = y(x) = (3*exp(-2*x))/4 + 17/4 - (3*x)/2 + x^2/2 - 4*exp(-x)

But I need the expression for N__1 to define some initial values for N__2 and so on, what I have tried that doesn’t work is.

C := eval(N__1, x = 200)

N__2 := C   - Just because nothing much is happing on this interval

And it gives me the following expression for N2

N__2 := y(200) = (3*exp(-400))/4 + 78817/4 - 4*exp(-200)

I then make a piecewise function to make a function which I can plot, which use to work.                         

N := piecewise(0 <= x and x <= 200, N__1, 200 <= x and x <= 1000, eval(N__2, x = x - 200))

But when I’m trying to plot N maple gives me a warning message.

plot(N, x = 0 .. 1000)

Warning, expecting only range or variable x in expression piecewise ………………..  to be plotted but found name y.

If I define N__1 as the solution to the differential equation, then it works just fine.

N__1 := (3*exp(-2*x))/4 + 17/4 - (3*x)/2 + x^2/2 - 4*exp(-x)

But that I’m not interested in, because that will require a lot of copy paste all the time.

Thank you in advance.

how I can convert this maple code to Matlab ones?

1.mw
 

restart; t1 := time(); with(LinearAlgebra); J := readstat("Please enter integer number J: "); N1 := proc (x) options operator, arrow; piecewise(0 <= x and x <= 1, 1) end proc; N2 := proc (x) options operator, arrow; piecewise(0 <= x and x <= 1, x, 1 < x and x <= 2, 2-x) end proc; N := proc (J, k) options operator, arrow; unapply(N2(2^J*x-k), x) end proc; Phi := proc (J, k) options operator, arrow; evalf((N(J, k))(x))*N1(x) end proc; PhiJ := Vector[column](2^J+1); for k from -1 to 2^J-1 do PhiJ[k+2] := Phi(J, k) end do; P := Matrix(2^J+1, 2^J+1); Map2[proc (i, j) options operator, arrow; evalb(i-j = 1) end proc](proc (x, a) options operator, arrow; x end proc, 1/6, P, inplace); Map2[proc (i, j) options operator, arrow; evalb(j-i = 1) end proc](proc (x, a) options operator, arrow; x end proc, 1/6, P, inplace); Map2[proc (i, j) options operator, arrow; evalb(i = j) end proc](proc (x, a) options operator, arrow; x end proc, 2/3, P, inplace); P[1, 1] := 1/3; P[2^J+1, 2^J+1] := 1/3; P := 2^(-J)*P; E := Matrix(2^J+1, 2^J+1); Map2[proc (i, j) options operator, arrow; evalb(i-j = 1) end proc](proc (x, a) options operator, arrow; x end proc, 1/2, E, inplace); Map2[proc (i, j) options operator, arrow; evalb(j-i = 1) end proc](proc (x, a) options operator, arrow; x end proc, -1/2, E, inplace); E[1, 1] := -1/2; E[2^J+1, 2^J+1] := 1/2; DPhi := E.(1/P); X1 := Vector[column](2^J+1, symbol = x1); X2 := Vector[column](2^J+1, symbol = x2); U := Vector[column](2^J+1, symbol = u); JJ := (1/2)*U^%T.P.U; x1t := X1^%T.PhiJ; x2t := X2^%T.PhiJ; ut := U^%T.PhiJ; for i from 0 to 2^J do PhiJxJ[i+1] := apply(unapply(PhiJ, x), i/2^J) end do; for i to 2^J+1 do eq1[i] := (X1^%T.DPhi-X2^%T).PhiJxJ[i] = 0; eq2[i] := (X2^%T.DPhi-U^%T).PhiJxJ[i] = 0 end do; for i to 2^J+1 do eq3[i] := X1^%T.PhiJxJ[i]-.1, 0 end do; eq1[0] := eval(x1t, x = 0) = 0; eq2[0] := eval(x2t, x = 0)-1 = 0; eq1[2^J+2] := eval(x1t, x = 1) = 0; eq2[2^J+2] := eval(x2t, x = 1) = -1; eqq1 := {seq(eq1[i], i = 0*.2^J+2)}; eqq2 := {seq(eq2[i], i = 0.2^J+2)}; eqq3 := {seq(eq3[i], i = 1.2^J+1)}; eq := `union`(`union`(eqq1, eqq2), eqq3); with(Optimization); S := NLPSolve(JJ, eq); assign(S[2]); uexact := piecewise(0 <= x and x <= .3, (200/9)*x-20/3, .3 <= x and x <= .7, 0, .7 <= x and x <= 1, -(200/9)*x+140/9); x2exact := piecewise(0 <= x and x <= .3, (100/9)*x^2-(20/3)*x+1, .3 <= x and x <= .7, 0, .7 <= x and x <= 1, -(100/9)*x^2+(140/9)*x-49/9); x1exact := piecewise(0 <= x and x <= .3, (100/27)*x^3-(10/3)*x^2+x, .3 <= x and x <= .7, 1/10, .7 <= x and x <= 1, -(100/27)*x^3+(70/9)*x^2-(49/9)*x+37/27); plot([x1exact, x1t], x = 0 .. 1, style = [line, point], legend = ["Exact", "Approximate"], axis = [gridlines = [colour = green, majorlines = 2]], labels = ["t", x[1](t)], labeldirections = ["horizontal", "vertical"])

t1 := 38.500

 

[`&x`, Add, Adjoint, BackwardSubstitute, BandMatrix, Basis, BezoutMatrix, BidiagonalForm, BilinearForm, CARE, CharacteristicMatrix, CharacteristicPolynomial, Column, ColumnDimension, ColumnOperation, ColumnSpace, CompanionMatrix, CompressedSparseForm, ConditionNumber, ConstantMatrix, ConstantVector, Copy, CreatePermutation, CrossProduct, DARE, DeleteColumn, DeleteRow, Determinant, Diagonal, DiagonalMatrix, Dimension, Dimensions, DotProduct, EigenConditionNumbers, Eigenvalues, Eigenvectors, Equal, ForwardSubstitute, FrobeniusForm, FromCompressedSparseForm, FromSplitForm, GaussianElimination, GenerateEquations, GenerateMatrix, Generic, GetResultDataType, GetResultShape, GivensRotationMatrix, GramSchmidt, HankelMatrix, HermiteForm, HermitianTranspose, HessenbergForm, HilbertMatrix, HouseholderMatrix, IdentityMatrix, IntersectionBasis, IsDefinite, IsOrthogonal, IsSimilar, IsUnitary, JordanBlockMatrix, JordanForm, KroneckerProduct, LA_Main, LUDecomposition, LeastSquares, LinearSolve, LyapunovSolve, Map, Map2, MatrixAdd, MatrixExponential, MatrixFunction, MatrixInverse, MatrixMatrixMultiply, MatrixNorm, MatrixPower, MatrixScalarMultiply, MatrixVectorMultiply, MinimalPolynomial, Minor, Modular, Multiply, NoUserValue, Norm, Normalize, NullSpace, OuterProductMatrix, Permanent, Pivot, PopovForm, ProjectionMatrix, QRDecomposition, RandomMatrix, RandomVector, Rank, RationalCanonicalForm, ReducedRowEchelonForm, Row, RowDimension, RowOperation, RowSpace, ScalarMatrix, ScalarMultiply, ScalarVector, SchurForm, SingularValues, SmithForm, SplitForm, StronglyConnectedBlocks, SubMatrix, SubVector, SumBasis, SylvesterMatrix, SylvesterSolve, ToeplitzMatrix, Trace, Transpose, TridiagonalForm, UnitVector, VandermondeMatrix, VectorAdd, VectorAngle, VectorMatrixMultiply, VectorNorm, VectorScalarMultiply, ZeroMatrix, ZeroVector, Zip]

 

J := 4

 

N1 := proc (x) options operator, arrow; piecewise(0 <= x and x <= 1, 1) end proc

 

N2 := proc (x) options operator, arrow; piecewise(0 <= x and x <= 1, x, 1 < x and x <= 2, 2-x) end proc

 

N := proc (J, k) options operator, arrow; unapply(N2(2^J*x-k), x) end proc

 

Phi := proc (J, k) options operator, arrow; evalf((N(J, k))(x))*N1(x) end proc

 

_rtable[36893490566539206892]

 

PhiJ[1] := piecewise(16.*x <= 0. and 0. <= 16.*x+1., 16.*x+1., 0. < 16.*x and 16.*x <= 1., 1.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[2] := piecewise(0. <= 16.*x and 16.*x <= 1., 16.*x, 1. < 16.*x and 16.*x <= 2., 2.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[3] := piecewise(`and`(0. <= 16.*x-1., 16.*x <= 2.), 16.*x-1., `and`(0. < 16.*x-2., 16.*x <= 3.), 3.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[4] := piecewise(`and`(0. <= 16.*x-2., 16.*x <= 3.), 16.*x-2., `and`(0. < 16.*x-3., 16.*x <= 4.), 4.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[5] := piecewise(`and`(0. <= 16.*x-3., 16.*x <= 4.), 16.*x-3., `and`(0. < 16.*x-4., 16.*x <= 5.), 5.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[6] := piecewise(`and`(0. <= 16.*x-4., 16.*x <= 5.), 16.*x-4., `and`(0. < 16.*x-5., 16.*x <= 6.), 6.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[7] := piecewise(`and`(0. <= 16.*x-5., 16.*x <= 6.), 16.*x-5., `and`(0. < 16.*x-6., 16.*x <= 7.), 7.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[8] := piecewise(`and`(0. <= 16.*x-6., 16.*x <= 7.), 16.*x-6., `and`(0. < 16.*x-7., 16.*x <= 8.), 8.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[9] := piecewise(`and`(0. <= 16.*x-7., 16.*x <= 8.), 16.*x-7., `and`(0. < 16.*x-8., 16.*x <= 9.), 9.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[10] := piecewise(`and`(0. <= 16.*x-8., 16.*x <= 9.), 16.*x-8., `and`(0. < 16.*x-9., 16.*x <= 10.), 10.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[11] := piecewise(`and`(0. <= 16.*x-9., 16.*x <= 10.), 16.*x-9., `and`(0. < 16.*x-10., 16.*x <= 11.), 11.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[12] := piecewise(`and`(0. <= 16.*x-10., 16.*x <= 11.), 16.*x-10., `and`(0. < 16.*x-11., 16.*x <= 12.), 12.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[13] := piecewise(`and`(0. <= 16.*x-11., 16.*x <= 12.), 16.*x-11., `and`(0. < 16.*x-12., 16.*x <= 13.), 13.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[14] := piecewise(`and`(0. <= 16.*x-12., 16.*x <= 13.), 16.*x-12., `and`(0. < 16.*x-13., 16.*x <= 14.), 14.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[15] := piecewise(`and`(0. <= 16.*x-13., 16.*x <= 14.), 16.*x-13., `and`(0. < 16.*x-14., 16.*x <= 15.), 15.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[16] := piecewise(`and`(0. <= 16.*x-14., 16.*x <= 15.), 16.*x-14., `and`(0. < 16.*x-15., 16.*x <= 16.), 16.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

PhiJ[17] := piecewise(`and`(0. <= 16.*x-15., 16.*x <= 16.), 16.*x-15., `and`(0. < 16.*x-16., 16.*x <= 17.), 17.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

_rtable[36893490566536466172]

 

_rtable[36893490566536466172]

 

_rtable[36893490566536466172]

 

_rtable[36893490566536466172]

 

P[1, 1] := 1/3

 

P[17, 17] := 1/3

 

_rtable[36893490566563670004]

 

_rtable[36893490566563682652]

 

_rtable[36893490566563682652]

 

_rtable[36893490566563682652]

 

E[1, 1] := -1/2

 

E[17, 17] := 1/2

 

_rtable[36893490566592433196]

 

_rtable[36893490566592446084]

 

_rtable[36893490566592458492]

 

_rtable[36893490566592462708]

 

JJ := ((1/96)*u[1]+(1/192)*u[2])*u[1]+((1/192)*u[1]+(1/48)*u[2]+(1/192)*u[3])*u[2]+((1/192)*u[2]+(1/48)*u[3]+(1/192)*u[4])*u[3]+((1/192)*u[3]+(1/48)*u[4]+(1/192)*u[5])*u[4]+((1/192)*u[4]+(1/48)*u[5]+(1/192)*u[6])*u[5]+((1/192)*u[5]+(1/48)*u[6]+(1/192)*u[7])*u[6]+((1/192)*u[6]+(1/48)*u[7]+(1/192)*u[8])*u[7]+((1/192)*u[7]+(1/48)*u[8]+(1/192)*u[9])*u[8]+((1/192)*u[8]+(1/48)*u[9]+(1/192)*u[10])*u[9]+((1/192)*u[9]+(1/48)*u[10]+(1/192)*u[11])*u[10]+((1/192)*u[10]+(1/48)*u[11]+(1/192)*u[12])*u[11]+((1/192)*u[11]+(1/48)*u[12]+(1/192)*u[13])*u[12]+((1/192)*u[12]+(1/48)*u[13]+(1/192)*u[14])*u[13]+((1/192)*u[13]+(1/48)*u[14]+(1/192)*u[15])*u[14]+((1/192)*u[14]+(1/48)*u[15]+(1/192)*u[16])*u[15]+((1/192)*u[15]+(1/48)*u[16]+(1/192)*u[17])*u[16]+((1/192)*u[16]+(1/96)*u[17])*u[17]

 

x1t := x1[1]*piecewise(16.*x <= 0. and 0. <= 16.*x+1., 16.*x+1., 0. < 16.*x and 16.*x <= 1., 1.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[2]*piecewise(0. <= 16.*x and 16.*x <= 1., 16.*x, 1. < 16.*x and 16.*x <= 2., 2.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[3]*piecewise(`and`(0. <= 16.*x-1., 16.*x <= 2.), 16.*x-1., `and`(0. < 16.*x-2., 16.*x <= 3.), 3.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[4]*piecewise(`and`(0. <= 16.*x-2., 16.*x <= 3.), 16.*x-2., `and`(0. < 16.*x-3., 16.*x <= 4.), 4.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[5]*piecewise(`and`(0. <= 16.*x-3., 16.*x <= 4.), 16.*x-3., `and`(0. < 16.*x-4., 16.*x <= 5.), 5.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[6]*piecewise(`and`(0. <= 16.*x-4., 16.*x <= 5.), 16.*x-4., `and`(0. < 16.*x-5., 16.*x <= 6.), 6.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[7]*piecewise(`and`(0. <= 16.*x-5., 16.*x <= 6.), 16.*x-5., `and`(0. < 16.*x-6., 16.*x <= 7.), 7.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[8]*piecewise(`and`(0. <= 16.*x-6., 16.*x <= 7.), 16.*x-6., `and`(0. < 16.*x-7., 16.*x <= 8.), 8.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[9]*piecewise(`and`(0. <= 16.*x-7., 16.*x <= 8.), 16.*x-7., `and`(0. < 16.*x-8., 16.*x <= 9.), 9.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[10]*piecewise(`and`(0. <= 16.*x-8., 16.*x <= 9.), 16.*x-8., `and`(0. < 16.*x-9., 16.*x <= 10.), 10.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[11]*piecewise(`and`(0. <= 16.*x-9., 16.*x <= 10.), 16.*x-9., `and`(0. < 16.*x-10., 16.*x <= 11.), 11.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[12]*piecewise(`and`(0. <= 16.*x-10., 16.*x <= 11.), 16.*x-10., `and`(0. < 16.*x-11., 16.*x <= 12.), 12.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[13]*piecewise(`and`(0. <= 16.*x-11., 16.*x <= 12.), 16.*x-11., `and`(0. < 16.*x-12., 16.*x <= 13.), 13.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[14]*piecewise(`and`(0. <= 16.*x-12., 16.*x <= 13.), 16.*x-12., `and`(0. < 16.*x-13., 16.*x <= 14.), 14.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[15]*piecewise(`and`(0. <= 16.*x-13., 16.*x <= 14.), 16.*x-13., `and`(0. < 16.*x-14., 16.*x <= 15.), 15.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[16]*piecewise(`and`(0. <= 16.*x-14., 16.*x <= 15.), 16.*x-14., `and`(0. < 16.*x-15., 16.*x <= 16.), 16.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x1[17]*piecewise(`and`(0. <= 16.*x-15., 16.*x <= 16.), 16.*x-15., `and`(0. < 16.*x-16., 16.*x <= 17.), 17.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

x2t := x2[1]*piecewise(16.*x <= 0. and 0. <= 16.*x+1., 16.*x+1., 0. < 16.*x and 16.*x <= 1., 1.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[2]*piecewise(0. <= 16.*x and 16.*x <= 1., 16.*x, 1. < 16.*x and 16.*x <= 2., 2.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[3]*piecewise(`and`(0. <= 16.*x-1., 16.*x <= 2.), 16.*x-1., `and`(0. < 16.*x-2., 16.*x <= 3.), 3.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[4]*piecewise(`and`(0. <= 16.*x-2., 16.*x <= 3.), 16.*x-2., `and`(0. < 16.*x-3., 16.*x <= 4.), 4.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[5]*piecewise(`and`(0. <= 16.*x-3., 16.*x <= 4.), 16.*x-3., `and`(0. < 16.*x-4., 16.*x <= 5.), 5.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[6]*piecewise(`and`(0. <= 16.*x-4., 16.*x <= 5.), 16.*x-4., `and`(0. < 16.*x-5., 16.*x <= 6.), 6.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[7]*piecewise(`and`(0. <= 16.*x-5., 16.*x <= 6.), 16.*x-5., `and`(0. < 16.*x-6., 16.*x <= 7.), 7.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[8]*piecewise(`and`(0. <= 16.*x-6., 16.*x <= 7.), 16.*x-6., `and`(0. < 16.*x-7., 16.*x <= 8.), 8.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[9]*piecewise(`and`(0. <= 16.*x-7., 16.*x <= 8.), 16.*x-7., `and`(0. < 16.*x-8., 16.*x <= 9.), 9.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[10]*piecewise(`and`(0. <= 16.*x-8., 16.*x <= 9.), 16.*x-8., `and`(0. < 16.*x-9., 16.*x <= 10.), 10.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[11]*piecewise(`and`(0. <= 16.*x-9., 16.*x <= 10.), 16.*x-9., `and`(0. < 16.*x-10., 16.*x <= 11.), 11.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[12]*piecewise(`and`(0. <= 16.*x-10., 16.*x <= 11.), 16.*x-10., `and`(0. < 16.*x-11., 16.*x <= 12.), 12.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[13]*piecewise(`and`(0. <= 16.*x-11., 16.*x <= 12.), 16.*x-11., `and`(0. < 16.*x-12., 16.*x <= 13.), 13.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[14]*piecewise(`and`(0. <= 16.*x-12., 16.*x <= 13.), 16.*x-12., `and`(0. < 16.*x-13., 16.*x <= 14.), 14.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[15]*piecewise(`and`(0. <= 16.*x-13., 16.*x <= 14.), 16.*x-13., `and`(0. < 16.*x-14., 16.*x <= 15.), 15.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[16]*piecewise(`and`(0. <= 16.*x-14., 16.*x <= 15.), 16.*x-14., `and`(0. < 16.*x-15., 16.*x <= 16.), 16.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+x2[17]*piecewise(`and`(0. <= 16.*x-15., 16.*x <= 16.), 16.*x-15., `and`(0. < 16.*x-16., 16.*x <= 17.), 17.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

ut := u[1]*piecewise(16.*x <= 0. and 0. <= 16.*x+1., 16.*x+1., 0. < 16.*x and 16.*x <= 1., 1.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[2]*piecewise(0. <= 16.*x and 16.*x <= 1., 16.*x, 1. < 16.*x and 16.*x <= 2., 2.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[3]*piecewise(`and`(0. <= 16.*x-1., 16.*x <= 2.), 16.*x-1., `and`(0. < 16.*x-2., 16.*x <= 3.), 3.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[4]*piecewise(`and`(0. <= 16.*x-2., 16.*x <= 3.), 16.*x-2., `and`(0. < 16.*x-3., 16.*x <= 4.), 4.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[5]*piecewise(`and`(0. <= 16.*x-3., 16.*x <= 4.), 16.*x-3., `and`(0. < 16.*x-4., 16.*x <= 5.), 5.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[6]*piecewise(`and`(0. <= 16.*x-4., 16.*x <= 5.), 16.*x-4., `and`(0. < 16.*x-5., 16.*x <= 6.), 6.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[7]*piecewise(`and`(0. <= 16.*x-5., 16.*x <= 6.), 16.*x-5., `and`(0. < 16.*x-6., 16.*x <= 7.), 7.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[8]*piecewise(`and`(0. <= 16.*x-6., 16.*x <= 7.), 16.*x-6., `and`(0. < 16.*x-7., 16.*x <= 8.), 8.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[9]*piecewise(`and`(0. <= 16.*x-7., 16.*x <= 8.), 16.*x-7., `and`(0. < 16.*x-8., 16.*x <= 9.), 9.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[10]*piecewise(`and`(0. <= 16.*x-8., 16.*x <= 9.), 16.*x-8., `and`(0. < 16.*x-9., 16.*x <= 10.), 10.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[11]*piecewise(`and`(0. <= 16.*x-9., 16.*x <= 10.), 16.*x-9., `and`(0. < 16.*x-10., 16.*x <= 11.), 11.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[12]*piecewise(`and`(0. <= 16.*x-10., 16.*x <= 11.), 16.*x-10., `and`(0. < 16.*x-11., 16.*x <= 12.), 12.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[13]*piecewise(`and`(0. <= 16.*x-11., 16.*x <= 12.), 16.*x-11., `and`(0. < 16.*x-12., 16.*x <= 13.), 13.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[14]*piecewise(`and`(0. <= 16.*x-12., 16.*x <= 13.), 16.*x-12., `and`(0. < 16.*x-13., 16.*x <= 14.), 14.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[15]*piecewise(`and`(0. <= 16.*x-13., 16.*x <= 14.), 16.*x-13., `and`(0. < 16.*x-14., 16.*x <= 15.), 15.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[16]*piecewise(`and`(0. <= 16.*x-14., 16.*x <= 15.), 16.*x-14., `and`(0. < 16.*x-15., 16.*x <= 16.), 16.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)+u[17]*piecewise(`and`(0. <= 16.*x-15., 16.*x <= 16.), 16.*x-15., `and`(0. < 16.*x-16., 16.*x <= 17.), 17.-16.*x)*piecewise(0 <= x and x <= 1, 1, 0)

 

_rtable[36893490566593511164]

 

_rtable[36893490566474385156]

 

_rtable[36893490566474387196]

 

_rtable[36893490566474274564]

 

_rtable[36893490566474276604]

 

_rtable[36893490566471927556]

 

_rtable[36893490566471929596]

 

_rtable[36893490566439466756]

 

_rtable[36893490566439468796]

 

_rtable[36893490566581483268]

 

_rtable[36893490566581485308]

 

_rtable[36893490566581487364]

 

_rtable[36893490566581489404]

 

_rtable[36893490566581491460]

 

_rtable[36893490566581493500]

 

_rtable[36893490566581561092]

 

_rtable[36893490566581563132]

 

eq1[1] := -20.28718708*x1[1]+25.72312247*x1[2]-6.892489894*x1[3]+1.846837101*x1[4]-.4948585097*x1[5]+.1325969380*x1[6]-0.3552924247e-1*x1[7]+0.9520031827e-2*x1[8]-0.2550884838e-2*x1[9]+0.6835075261e-3*x1[10]-0.1831452664e-3*x1[11]+0.4907353932e-4*x1[12]-0.1314889092e-4*x1[13]+3.522024353*10^(-6)*x1[14]-9.392064942*10^(-7)*x1[15]+2.348016235*10^(-7)*x1[16]-3.913360392*10^(-8)*x1[17]-1.*x2[1] = 0

 

eq2[1] := -20.28718708*x2[1]+25.72312247*x2[2]-6.892489894*x2[3]+1.846837101*x2[4]-.4948585097*x2[5]+.1325969380*x2[6]-0.3552924247e-1*x2[7]+0.9520031827e-2*x2[8]-0.2550884838e-2*x2[9]+0.6835075261e-3*x2[10]-0.1831452664e-3*x2[11]+0.4907353932e-4*x2[12]-0.1314889092e-4*x2[13]+3.522024353*10^(-6)*x2[14]-9.392064942*10^(-7)*x2[15]+2.348016235*10^(-7)*x2[16]-3.913360392*10^(-8)*x2[17]-1.*u[1] = 0

 

eq1[2] := -7.425625842*x1[1]-3.446244947*x1[2]+13.78497979*x1[3]-3.693674202*x1[4]+.9897170194*x1[5]-.2651938761*x1[6]+0.7105848494e-1*x1[7]-0.1904006365e-1*x1[8]+0.5101769676e-2*x1[9]-0.1367015052e-2*x1[10]+0.3662905327e-3*x1[11]-0.9814707864e-4*x1[12]+0.2629778184e-4*x1[13]-7.044048706*10^(-6)*x1[14]+1.878412988*10^(-6)*x1[15]-4.696032471*10^(-7)*x1[16]+7.826720785*10^(-8)*x1[17]-1.000000000*x2[2] = 0

 

eq2[2] := -7.425625842*x2[1]-3.446244947*x2[2]+13.78497979*x2[3]-3.693674202*x2[4]+.9897170194*x2[5]-.2651938761*x2[6]+0.7105848494e-1*x2[7]-0.1904006365e-1*x2[8]+0.5101769676e-2*x2[9]-0.1367015052e-2*x2[10]+0.3662905327e-3*x2[11]-0.9814707864e-4*x2[12]+0.2629778184e-4*x2[13]-7.044048706*10^(-6)*x2[14]+1.878412988*10^(-6)*x2[15]-4.696032471*10^(-7)*x2[16]+7.826720785*10^(-8)*x2[17]-1.000000000*u[2] = 0

 

eq1[3] := 1.989690448*x1[1]-11.93814269*x1[2]-.2474292549*x1[3]+12.92785971*x1[4]-3.464009568*x1[5]+.9281785663*x1[6]-.2487046973*x1[7]+0.6664022279e-1*x1[8]-0.1785619387e-1*x1[9]+0.4784552683e-2*x1[10]-0.1282016864e-2*x1[11]+0.3435147752e-3*x1[12]-0.9204223643e-4*x1[13]+0.2465417047e-4*x1[14]-6.574445459*10^(-6)*x1[15]+1.643611365*10^(-6)*x1[16]-2.739352275*10^(-7)*x1[17]-1.000000000*x2[3] = 0

 

eq2[3] := 1.989690448*x2[1]-11.93814269*x2[2]-.2474292549*x2[3]+12.92785971*x2[4]-3.464009568*x2[5]+.9281785663*x2[6]-.2487046973*x2[7]+0.6664022279e-1*x2[8]-0.1785619387e-1*x2[9]+0.4784552683e-2*x2[10]-0.1282016864e-2*x2[11]+0.3435147752e-3*x2[12]-0.9204223643e-4*x2[13]+0.2465417047e-4*x2[14]-6.574445459*10^(-6)*x2[15]+1.643611365*10^(-6)*x2[16]-2.739352275*10^(-7)*x2[17]-1.000000000*u[3] = 0

 

eq1[4] := -.5331359486*x1[1]+3.198815692*x1[2]-12.79526277*x1[3]-0.1776462123e-1*x1[4]+12.86632125*x1[5]-3.447520389*x1[6]+.9237603042*x1[7]-.2475208275*x1[8]+0.6632300579e-1*x1[9]-0.1777119568e-1*x1[10]+0.4761776926e-2*x1[11]-0.1275912022e-2*x1[12]+0.3418711639e-3*x1[13]-0.9157263318e-4*x1[14]+0.2441936885e-4*x1[15]-6.104842212*10^(-6)*x1[16]+1.017473702*10^(-6)*x1[17]-1.000000000*x2[4] = 0

 

eq2[4] := -.5331359486*x2[1]+3.198815692*x2[2]-12.79526277*x2[3]-0.1776462123e-1*x2[4]+12.86632125*x2[5]-3.447520389*x2[6]+.9237603042*x2[7]-.2475208275*x2[8]+0.6632300579e-1*x2[9]-0.1777119568e-1*x2[10]+0.4761776926e-2*x2[11]-0.1275912022e-2*x2[12]+0.3418711639e-3*x2[13]-0.9157263318e-4*x2[14]+0.2441936885e-4*x2[15]-6.104842212*10^(-6)*x2[16]+1.017473702*10^(-6)*x2[17]-1.000000000*u[4] = 0

 

eq1[5] := .1428533469*x1[1]-.8571200814*x1[2]+3.428480326*x1[3]-12.85680122*x1[4]-0.1275442419e-2*x1[5]+12.86190299*x1[6]-3.446336519*x1[7]+.9234430872*x1[8]-.2474358293*x1[9]+0.6630023004e-1*x1[10]-0.1776509084e-1*x1[11]+0.4760133314e-2*x1[12]-0.1275442419e-2*x1[13]+0.3416363623e-3*x1[14]-0.9110302994e-4*x1[15]+0.2277575748e-4*x1[16]-3.795959581*10^(-6)*x1[17]-1.000000000*x2[5] = 0

 

eq2[5] := .1428533469*x2[1]-.8571200814*x2[2]+3.428480326*x2[3]-12.85680122*x2[4]-0.1275442419e-2*x2[5]+12.86190299*x2[6]-3.446336519*x2[7]+.9234430872*x2[8]-.2474358293*x2[9]+0.6630023004e-1*x2[10]-0.1776509084e-1*x2[11]+0.4760133314e-2*x2[12]-0.1275442419e-2*x2[13]+0.3416363623e-3*x2[14]-0.9110302994e-4*x2[15]+0.2277575748e-4*x2[16]-3.795959581*10^(-6)*x2[17]-1.000000000*u[5] = 0

 

eq1[6] := -0.3827743894e-1*x1[1]+.2296646336*x1[2]-.9186585345*x1[3]+3.444969504*x1[4]-12.86121948*x1[5]-0.9157263318e-4*x1[6]+12.86158577*x1[7]-3.446251521*x1[8]+.9234203114*x1[9]-.2474297245*x1[10]+0.6629858642e-1*x1[11]-0.1776462123e-1*x1[12]+0.4759898512e-2*x1[13]-0.1274972816e-2*x1[14]+0.3399927509e-3*x1[15]-0.8499818772e-4*x1[16]+0.1416636462e-4*x1[17]-1.000000000*x2[6] = 0

 

eq2[6] := -0.3827743894e-1*x2[1]+.2296646336*x2[2]-.9186585345*x2[3]+3.444969504*x2[4]-12.86121948*x2[5]-0.9157263318e-4*x2[6]+12.86158577*x2[7]-3.446251521*x2[8]+.9234203114*x2[9]-.2474297245*x2[10]+0.6629858642e-1*x2[11]-0.1776462123e-1*x2[12]+0.4759898512e-2*x2[13]-0.1274972816e-2*x2[14]+0.3399927509e-3*x2[15]-0.8499818772e-4*x2[16]+0.1416636462e-4*x2[17]-1.000000000*u[6] = 0

 

eq1[7] := 0.1025640885e-1*x1[1]-0.6153845311e-1*x1[2]+.2461538124*x1[3]-.9230767966*x1[4]+3.446153374*x1[5]-12.86153670*x1[6]-6.574445459*10^(-6)*x1[7]+12.86156300*x1[8]-3.446245416*x1[9]+.9234186678*x1[10]-.2474292549*x1[11]+0.6629835162e-1*x1[12]-0.1776415163e-1*x1[13]+0.4758254901e-2*x1[14]-0.1268867974e-2*x1[15]+0.3172169934e-3*x1[16]-0.5286949890e-4*x1[17]-1.000000000*x2[7] = 0

 

eq2[7] := 0.1025640885e-1*x2[1]-0.6153845311e-1*x2[2]+.2461538124*x2[3]-.9230767966*x2[4]+3.446153374*x2[5]-12.86153670*x2[6]-6.574445459*10^(-6)*x2[7]+12.86156300*x2[8]-3.446245416*x2[9]+.9234186678*x2[10]-.2474292549*x2[11]+0.6629835162e-1*x2[12]-0.1776415163e-1*x2[13]+0.4758254901e-2*x2[14]-0.1268867974e-2*x2[15]+0.3172169934e-3*x2[16]-0.5286949890e-4*x2[17]-1.000000000*u[7] = 0

 

eq1[8] := -0.2748196469e-2*x1[1]+0.1648917882e-1*x1[2]-0.6595671526e-1*x1[3]+.2473376822*x1[4]-.9233940136*x1[5]+3.446238372*x1[6]-12.86155948*x1[7]-4.696032471*10^(-7)*x1[8]+12.86156135*x1[9]-3.446244947*x1[10]+.9234184330*x1[11]-.2474287852*x1[12]+0.6629670801e-1*x1[13]-0.1775804679e-1*x1[14]+0.4735479144e-2*x1[15]-0.1183869786e-2*x1[16]+0.1973116310e-3*x1[17]-1.000000000*x2[8] = 0

 

eq2[8] := -0.2748196469e-2*x2[1]+0.1648917882e-1*x2[2]-0.6595671526e-1*x2[3]+.2473376822*x2[4]-.9233940136*x2[5]+3.446238372*x2[6]-12.86155948*x2[7]-4.696032471*10^(-7)*x2[8]+12.86156135*x2[9]-3.446244947*x2[10]+.9234184330*x2[11]-.2474287852*x2[12]+0.6629670801e-1*x2[13]-0.1775804679e-1*x2[14]+0.4735479144e-2*x2[15]-0.1183869786e-2*x2[16]+0.1973116310e-3*x2[17]-1.000000000*u[8] = 0

 

eq1[9] := 0.7363770250e-3*x1[1]-0.4418262150e-2*x1[2]+0.1767304860e-1*x1[3]-0.6627393225e-1*x1[4]+.2474226804*x1[5]-.9234167894*x1[6]+3.446244477*x1[7]-12.86156112*x1[8]+12.86156112*x1[10]-3.446244477*x1[11]+.9234167894*x1[12]-.2474226804*x1[13]+0.6627393225e-1*x1[14]-0.1767304860e-1*x1[15]+0.4418262150e-2*x1[16]-0.7363770250e-3*x1[17]-1.000000000*x2[9] = 0

 

eq2[9] := 0.7363770250e-3*x2[1]-0.4418262150e-2*x2[2]+0.1767304860e-1*x2[3]-0.6627393225e-1*x2[4]+.2474226804*x2[5]-.9234167894*x2[6]+3.446244477*x2[7]-12.86156112*x2[8]+12.86156112*x2[10]-3.446244477*x2[11]+.9234167894*x2[12]-.2474226804*x2[13]+0.6627393225e-1*x2[14]-0.1767304860e-1*x2[15]+0.4418262150e-2*x2[16]-0.7363770250e-3*x2[17]-1.000000000*u[9] = 0

 

eq1[10] := -0.1973116310e-3*x1[1]+0.1183869786e-2*x1[2]-0.4735479144e-2*x1[3]+0.1775804679e-1*x1[4]-0.6629670801e-1*x1[5]+.2474287852*x1[6]-.9234184330*x1[7]+3.446244947*x1[8]-12.86156135*x1[9]+4.696032471*10^(-7)*x1[10]+12.86155948*x1[11]-3.446238372*x1[12]+.9233940136*x1[13]-.2473376822*x1[14]+0.6595671526e-1*x1[15]-0.1648917882e-1*x1[16]+0.2748196469e-2*x1[17]-1.000000000*x2[10] = 0

 

eq2[10] := -0.1973116310e-3*x2[1]+0.1183869786e-2*x2[2]-0.4735479144e-2*x2[3]+0.1775804679e-1*x2[4]-0.6629670801e-1*x2[5]+.2474287852*x2[6]-.9234184330*x2[7]+3.446244947*x2[8]-12.86156135*x2[9]+4.696032471*10^(-7)*x2[10]+12.86155948*x2[11]-3.446238372*x2[12]+.9233940136*x2[13]-.2473376822*x2[14]+0.6595671526e-1*x2[15]-0.1648917882e-1*x2[16]+0.2748196469e-2*x2[17]-1.000000000*u[10] = 0

 

eq1[11] := 0.5286949890e-4*x1[1]-0.3172169934e-3*x1[2]+0.1268867974e-2*x1[3]-0.4758254901e-2*x1[4]+0.1776415163e-1*x1[5]-0.6629835162e-1*x1[6]+.2474292549*x1[7]-.9234186678*x1[8]+3.446245416*x1[9]-12.86156300*x1[10]+6.574445459*10^(-6)*x1[11]+12.86153670*x1[12]-3.446153374*x1[13]+.9230767966*x1[14]-.2461538124*x1[15]+0.6153845311e-1*x1[16]-0.1025640885e-1*x1[17]-1.00000000*x2[11] = 0

 

eq2[11] := 0.5286949890e-4*x2[1]-0.3172169934e-3*x2[2]+0.1268867974e-2*x2[3]-0.4758254901e-2*x2[4]+0.1776415163e-1*x2[5]-0.6629835162e-1*x2[6]+.2474292549*x2[7]-.9234186678*x2[8]+3.446245416*x2[9]-12.86156300*x2[10]+6.574445459*10^(-6)*x2[11]+12.86153670*x2[12]-3.446153374*x2[13]+.9230767966*x2[14]-.2461538124*x2[15]+0.6153845311e-1*x2[16]-0.1025640885e-1*x2[17]-1.00000000*u[11] = 0

 

eq1[12] := -0.1416636462e-4*x1[1]+0.8499818772e-4*x1[2]-0.3399927509e-3*x1[3]+0.1274972816e-2*x1[4]-0.4759898512e-2*x1[5]+0.1776462123e-1*x1[6]-0.6629858642e-1*x1[7]+.2474297245*x1[8]-.9234203114*x1[9]+3.446251521*x1[10]-12.86158577*x1[11]+0.9157263318e-4*x1[12]+12.86121948*x1[13]-3.444969504*x1[14]+.9186585345*x1[15]-.2296646336*x1[16]+0.3827743894e-1*x1[17]-1.00000000*x2[12] = 0

 

eq2[12] := -0.1416636462e-4*x2[1]+0.8499818772e-4*x2[2]-0.3399927509e-3*x2[3]+0.1274972816e-2*x2[4]-0.4759898512e-2*x2[5]+0.1776462123e-1*x2[6]-0.6629858642e-1*x2[7]+.2474297245*x2[8]-.9234203114*x2[9]+3.446251521*x2[10]-12.86158577*x2[11]+0.9157263318e-4*x2[12]+12.86121948*x2[13]-3.444969504*x2[14]+.9186585345*x2[15]-.2296646336*x2[16]+0.3827743894e-1*x2[17]-1.00000000*u[12] = 0

 

eq1[13] := 3.795959581*10^(-6)*x1[1]-0.2277575748e-4*x1[2]+0.9110302994e-4*x1[3]-0.3416363623e-3*x1[4]+0.1275442419e-2*x1[5]-0.4760133314e-2*x1[6]+0.1776509084e-1*x1[7]-0.6630023004e-1*x1[8]+.2474358293*x1[9]-.9234430872*x1[10]+3.446336519*x1[11]-12.86190299*x1[12]+0.1275442419e-2*x1[13]+12.85680122*x1[14]-3.428480326*x1[15]+.8571200814*x1[16]-.1428533469*x1[17]-1.00000000*x2[13] = 0

 

eq2[13] := 3.795959581*10^(-6)*x2[1]-0.2277575748e-4*x2[2]+0.9110302994e-4*x2[3]-0.3416363623e-3*x2[4]+0.1275442419e-2*x2[5]-0.4760133314e-2*x2[6]+0.1776509084e-1*x2[7]-0.6630023004e-1*x2[8]+.2474358293*x2[9]-.9234430872*x2[10]+3.446336519*x2[11]-12.86190299*x2[12]+0.1275442419e-2*x2[13]+12.85680122*x2[14]-3.428480326*x2[15]+.8571200814*x2[16]-.1428533469*x2[17]-1.00000000*u[13] = 0

 

eq1[14] := -1.017473702*10^(-6)*x1[1]+6.104842212*10^(-6)*x1[2]-0.2441936885e-4*x1[3]+0.9157263318e-4*x1[4]-0.3418711639e-3*x1[5]+0.1275912022e-2*x1[6]-0.4761776926e-2*x1[7]+0.1777119568e-1*x1[8]-0.6632300579e-1*x1[9]+.2475208275*x1[10]-.9237603042*x1[11]+3.447520389*x1[12]-12.86632125*x1[13]+0.1776462123e-1*x1[14]+12.79526277*x1[15]-3.198815692*x1[16]+.5331359486*x1[17]-1.00000000*x2[14] = 0

 

eq2[14] := -1.017473702*10^(-6)*x2[1]+6.104842212*10^(-6)*x2[2]-0.2441936885e-4*x2[3]+0.9157263318e-4*x2[4]-0.3418711639e-3*x2[5]+0.1275912022e-2*x2[6]-0.4761776926e-2*x2[7]+0.1777119568e-1*x2[8]-0.6632300579e-1*x2[9]+.2475208275*x2[10]-.9237603042*x2[11]+3.447520389*x2[12]-12.86632125*x2[13]+0.1776462123e-1*x2[14]+12.79526277*x2[15]-3.198815692*x2[16]+.5331359486*x2[17]-1.00000000*u[14] = 0

 

eq1[15] := 2.739352275*10^(-7)*x1[1]-1.643611365*10^(-6)*x1[2]+6.574445459*10^(-6)*x1[3]-0.2465417047e-4*x1[4]+0.9204223643e-4*x1[5]-0.3435147752e-3*x1[6]+0.1282016864e-2*x1[7]-0.4784552683e-2*x1[8]+0.1785619387e-1*x1[9]-0.6664022279e-1*x1[10]+.2487046973*x1[11]-.9281785663*x1[12]+3.464009568*x1[13]-12.92785971*x1[14]+.2474292549*x1[15]+11.93814269*x1[16]-1.989690448*x1[17]-1.00000000*x2[15] = 0

 

eq2[15] := 2.739352275*10^(-7)*x2[1]-1.643611365*10^(-6)*x2[2]+6.574445459*10^(-6)*x2[3]-0.2465417047e-4*x2[4]+0.9204223643e-4*x2[5]-0.3435147752e-3*x2[6]+0.1282016864e-2*x2[7]-0.4784552683e-2*x2[8]+0.1785619387e-1*x2[9]-0.6664022279e-1*x2[10]+.2487046973*x2[11]-.9281785663*x2[12]+3.464009568*x2[13]-12.92785971*x2[14]+.2474292549*x2[15]+11.93814269*x2[16]-1.989690448*x2[17]-1.00000000*u[15] = 0

 

eq1[16] := -7.826720785*10^(-8)*x1[1]+4.696032471*10^(-7)*x1[2]-1.878412988*10^(-6)*x1[3]+7.044048706*10^(-6)*x1[4]-0.2629778184e-4*x1[5]+0.9814707864e-4*x1[6]-0.3662905327e-3*x1[7]+0.1367015052e-2*x1[8]-0.5101769676e-2*x1[9]+0.1904006365e-1*x1[10]-0.7105848494e-1*x1[11]+.2651938761*x1[12]-.9897170194*x1[13]+3.693674202*x1[14]-13.78497979*x1[15]+3.446244947*x1[16]+7.425625842*x1[17]-1.00000000*x2[16] = 0

 

eq2[16] := -7.826720785*10^(-8)*x2[1]+4.696032471*10^(-7)*x2[2]-1.878412988*10^(-6)*x2[3]+7.044048706*10^(-6)*x2[4]-0.2629778184e-4*x2[5]+0.9814707864e-4*x2[6]-0.3662905327e-3*x2[7]+0.1367015052e-2*x2[8]-0.5101769676e-2*x2[9]+0.1904006365e-1*x2[10]-0.7105848494e-1*x2[11]+.2651938761*x2[12]-.9897170194*x2[13]+3.693674202*x2[14]-13.78497979*x2[15]+3.446244947*x2[16]+7.425625842*x2[17]-1.00000000*u[16] = 0

 

eq1[17] := 3.913360392*10^(-8)*x1[1]-2.348016235*10^(-7)*x1[2]+9.392064942*10^(-7)*x1[3]-3.522024353*10^(-6)*x1[4]+0.1314889092e-4*x1[5]-0.4907353932e-4*x1[6]+0.1831452664e-3*x1[7]-0.6835075261e-3*x1[8]+0.2550884838e-2*x1[9]-0.9520031827e-2*x1[10]+0.3552924247e-1*x1[11]-.1325969380*x1[12]+.4948585097*x1[13]-1.846837101*x1[14]+6.892489894*x1[15]-25.72312247*x1[16]+20.28718708*x1[17]-1.*x2[17] = 0

 

eq2[17] := 3.913360392*10^(-8)*x2[1]-2.348016235*10^(-7)*x2[2]+9.392064942*10^(-7)*x2[3]-3.522024353*10^(-6)*x2[4]+0.1314889092e-4*x2[5]-0.4907353932e-4*x2[6]+0.1831452664e-3*x2[7]-0.6835075261e-3*x2[8]+0.2550884838e-2*x2[9]-0.9520031827e-2*x2[10]+0.3552924247e-1*x2[11]-.1325969380*x2[12]+.4948585097*x2[13]-1.846837101*x2[14]+6.892489894*x2[15]-25.72312247*x2[16]+20.28718708*x2[17]-1.*u[17] = 0

 

eq3[1] := 1.*x1[1]-.1, 0

 

eq3[2] := 1.000000000*x1[2]-.1, 0

 

eq3[3] := 1.000000000*x1[3]-.1, 0

 

eq3[4] := 1.000000000*x1[4]-.1, 0

 

eq3[5] := 1.000000000*x1[5]-.1, 0

 

eq3[6] := 1.000000000*x1[6]-.1, 0

 

eq3[7] := 1.000000000*x1[7]-.1, 0

 

eq3[8] := 1.000000000*x1[8]-.1, 0

 

eq3[9] := 1.000000000*x1[9]-.1, 0

 

eq3[10] := 1.000000000*x1[10]-.1, 0

 

eq3[11] := 1.00000000*x1[11]-.1, 0

 

eq3[12] := 1.00000000*x1[12]-.1, 0

 

eq3[13] := 1.00000000*x1[13]-.1, 0

 

eq3[14] := 1.00000000*x1[14]-.1, 0

 

eq3[15] := 1.00000000*x1[15]-.1, 0

 

eq3[16] := 1.00000000*x1[16]-.1, 0

 

eq3[17] := 1.*x1[17]-.1, 0

 

eq1[0] := 1.*x1[1] = 0

 

eq2[0] := 1.*x2[1]-1 = 0

 

eq1[18] := 1.*x1[17] = 0

 

eq2[18] := 1.*x2[17] = -1

 

eqq1 := {-7.425625842*x1[1]-3.446244947*x1[2]+13.78497979*x1[3]-3.693674202*x1[4]+.9897170194*x1[5]-.2651938761*x1[6]+0.7105848494e-1*x1[7]-0.1904006365e-1*x1[8]+0.5101769676e-2*x1[9]-0.1367015052e-2*x1[10]+0.3662905327e-3*x1[11]-0.9814707864e-4*x1[12]+0.2629778184e-4*x1[13]-7.044048706*10^(-6)*x1[14]+1.878412988*10^(-6)*x1[15]-4.696032471*10^(-7)*x1[16]+7.826720785*10^(-8)*x1[17]-1.000000000*x2[2] = 0}

 

eqq2 := {-7.425625842*x2[1]-3.446244947*x2[2]+13.78497979*x2[3]-3.693674202*x2[4]+.9897170194*x2[5]-.2651938761*x2[6]+0.7105848494e-1*x2[7]-0.1904006365e-1*x2[8]+0.5101769676e-2*x2[9]-0.1367015052e-2*x2[10]+0.3662905327e-3*x2[11]-0.9814707864e-4*x2[12]+0.2629778184e-4*x2[13]-7.044048706*10^(-6)*x2[14]+1.878412988*10^(-6)*x2[15]-4.696032471*10^(-7)*x2[16]+7.826720785*10^(-8)*x2[17]-1.000000000*u[2] = 0}

 

eqq3 := {0, 1.*x1[17]-.1}

 

eq := {0, 1.*x1[17]-.1, -7.425625842*x1[1]-3.446244947*x1[2]+13.78497979*x1[3]-3.693674202*x1[4]+.9897170194*x1[5]-.2651938761*x1[6]+0.7105848494e-1*x1[7]-0.1904006365e-1*x1[8]+0.5101769676e-2*x1[9]-0.1367015052e-2*x1[10]+0.3662905327e-3*x1[11]-0.9814707864e-4*x1[12]+0.2629778184e-4*x1[13]-7.044048706*10^(-6)*x1[14]+1.878412988*10^(-6)*x1[15]-4.696032471*10^(-7)*x1[16]+7.826720785*10^(-8)*x1[17]-1.000000000*x2[2] = 0, -7.425625842*x2[1]-3.446244947*x2[2]+13.78497979*x2[3]-3.693674202*x2[4]+.9897170194*x2[5]-.2651938761*x2[6]+0.7105848494e-1*x2[7]-0.1904006365e-1*x2[8]+0.5101769676e-2*x2[9]-0.1367015052e-2*x2[10]+0.3662905327e-3*x2[11]-0.9814707864e-4*x2[12]+0.2629778184e-4*x2[13]-7.044048706*10^(-6)*x2[14]+1.878412988*10^(-6)*x2[15]-4.696032471*10^(-7)*x2[16]+7.826720785*10^(-8)*x2[17]-1.000000000*u[2] = 0}

 

[ImportMPS, Interactive, LPSolve, LSSolve, Maximize, Minimize, NLPSolve, QPSolve]

 

Error, (in Optimization:-NLPSolve) constraints must be specified as a set or list of equalities and inequalities

 

uexact := piecewise(0 <= x and x <= .3, 200*x*(1/9)-20/3, .3 <= x and x <= .7, 0, .7 <= x and x <= 1, -200*x*(1/9)+140/9)

 

x2exact := piecewise(0 <= x and x <= .3, (100/9)*x^2-(20/3)*x+1, .3 <= x and x <= .7, 0, .7 <= x and x <= 1, -(100/9)*x^2+(140/9)*x-49/9)

 

piecewise(0 <= x and x <= .3, (100/27)*x^3-(10/3)*x^2+x, .3 <= x and x <= .7, 1/10, .7 <= x and x <= 1, -(100/27)*x^3+(70/9)*x^2-(49/9)*x+37/27)

 

Warning, expecting only range variable x in expression x1[1]*piecewise(0. <= 16.*x+1. and 16.*x <= 0.,16.*x+1.,0. < 16.*x and 16.*x <= 1.,1.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[2]*piecewise(0. <= 16.*x and 16.*x <= 1.,16.*x,1. < 16.*x and 16.*x <= 2.,2.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[3]*piecewise(0. <= 16.*x-1. and 16.*x <= 2.,16.*x-1.,0. < 16.*x-2. and 16.*x <= 3.,3.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[4]*piecewise(0. <= 16.*x-2. and 16.*x <= 3.,16.*x-2.,0. < 16.*x-3. and 16.*x <= 4.,4.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[5]*piecewise(0. <= 16.*x-3. and 16.*x <= 4.,16.*x-3.,0. < 16.*x-4. and 16.*x <= 5.,5.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[6]*piecewise(0. <= 16.*x-4. and 16.*x <= 5.,16.*x-4.,0. < 16.*x-5. and 16.*x <= 6.,6.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[7]*piecewise(0. <= 16.*x-5. and 16.*x <= 6.,16.*x-5.,0. < 16.*x-6. and 16.*x <= 7.,7.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[8]*piecewise(0. <= 16.*x-6. and 16.*x <= 7.,16.*x-6.,0. < 16.*x-7. and 16.*x <= 8.,8.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[9]*piecewise(0. <= 16.*x-7. and 16.*x <= 8.,16.*x-7.,0. < 16.*x-8. and 16.*x <= 9.,9.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[10]*piecewise(0. <= 16.*x-8. and 16.*x <= 9.,16.*x-8.,0. < 16.*x-9. and 16.*x <= 10.,10.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[11]*piecewise(0. <= 16.*x-9. and 16.*x <= 10.,16.*x-9.,0. < 16.*x-10. and 16.*x <= 11.,11.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[12]*piecewise(0. <= 16.*x-10. and 16.*x <= 11.,16.*x-10.,0. < 16.*x-11. and 16.*x <= 12.,12.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[13]*piecewise(0. <= 16.*x-11. and 16.*x <= 12.,16.*x-11.,0. < 16.*x-12. and 16.*x <= 13.,13.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[14]*piecewise(0. <= 16.*x-12. and 16.*x <= 13.,16.*x-12.,0. < 16.*x-13. and 16.*x <= 14.,14.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[15]*piecewise(0. <= 16.*x-13. and 16.*x <= 14.,16.*x-13.,0. < 16.*x-14. and 16.*x <= 15.,15.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[16]*piecewise(0. <= 16.*x-14. and 16.*x <= 15.,16.*x-14.,0. < 16.*x-15. and 16.*x <= 16.,16.-16.*x)*piecewise(0 <= x and x <= 1,1)+x1[17]*piecewise(0. <= 16.*x-15. and 16.*x <= 16.,16.*x-15.,0. < 16.*x-16. and 16.*x <= 17.,17.-16.*x)*piecewise(0 <= x and x <= 1,1) to be plotted but found names [x1[1], x1[2], x1[3], x1[4], x1[5], x1[6], x1[7], x1[8], x1[9], x1[10], x1[11], x1[12], x1[13], x1[14], x1[15], x1[16], x1[17]]

 

 

``


 

Download 1.mw

 

Hello Everyone;

Hope you are fine. I am applying rk-4 and Runge-Kutta-Fehlberg method for system of odes but there is no difference in the result of these method. Can anybody guide about that for my problem. I have uploaded the code. Thanks in advance.

Question#3.mw

 

Hello!

How do I solve this system? could someone help me please.

restart;
eqns := {x(t+1) = (1+10^(-6)*(0.4e-2-0.6e-2)/(0.1e-3))*x(t)+0.7e-1*10^(-6)*y(t),

y(t+1) = 0.6e-2*10^(-6)*x(t)/(0.1e-3)+(1-0.7e-1*10^(-6))*y(t)};

with:
x(0) = 1
y(0) =
y(0) = 857.1428571

restart

eqns := {x(t+1) = (1+10^(-6)*(0.4e-2-0.6e-2)/(0.1e-3))*x(t)+0.7e-1*10^(-6)*y(t), y(t+1) = 0.6e-2*10^(-6)*x(t)/(0.1e-3)+(1-0.7e-1*10^(-6))*y(t)};

{x(t+1) = .9999800000*x(t)+0.7000000000e-7*y(t), y(t+1) = 0.6000000000e-4*x(t)+.9999999300*y(t)}

(1)

x(0) = 1

x(0) = 1

(2)

y(0) = 0.6e-2/(0.1e-3*0.7e-1)

y(0) = 857.1428571

(3)

``

Download SystemRecursive.mw

Good day all.

My particular question concerns a Traveling Salesman-type problem.

Suppose I wish to move along the x-y plane and visit specific nodes (see the attached worksheet).

Starting at the origin, A, I intend to visit four locations, B to E, and finally return to the origin point. These nodes may be visited in any order - but, my total distance travelled must be a minimum.
However, my direction of travel is restricted; namely:

1. Movement is limited to the x and y-directions only (up and down as well as left and right)
2. Horizontal (left or right) movement is permitten only at y=1 and y=10

This second rule restricts me from turning left or right in between y= 1 and 10.
The Traveling Salesman routine (attached) is constructed to select a tour that is confined to orthogonal movement but it does not observe the second restriction (i.e. move left or right when you reach y=1 or y=10).

Is there any way in which I can build this condition into the routine so that the movement along the circuit observes the restrictions? 

If so - is it possible to graphically illustrate the order of travel (using arrows from point-to-point) on a point plot?

I appreciate you taking the time to read this.

MaplePrimes_May_6.mw

I tried to graph the function f(x)=x^(1/3) but it only gives me a graph for non-negative x's, when the function f has all real numbers as its domain.

Maybe I'm doing it wrong.

Thank you.

Hey I’m trying to shift a function horizontally to the right.

I have the following 3 functions:

V1 := -x

V2:= -2*x + 4.95

V3:= -x + 1.665

Function V2 I want to shift l_1 to the right, and V3 I want to shift l_1 + l_2 to the right.

I know I can do this manually by define the functions:

V2:= -2*(x-l_1) + 4.95

V3:= -(x-l_1-l_2) + 1.665

Where l_1:=1.665 and l_2:=4.95

Is there a way to do this without typing it manually?

By the way it’s only for plotting, so it’s not necessary for me to actually define them, if there is a way to do it within a plot some how.

Thank you in advance.

I am trying to solve these equation using fsolve, but it returns the same valve. Please provide a solution.\

Please see the attachment. 

Hi, with Units[Simple] package loaded, I tried to differentiate a few functions using D[n] but it does not work. problem.mw

a := proc (x, y) options operator, arrow; exp(x*y) end proc

proc (x, y) options operator, arrow; exp(y*x) end proc

(1)

D[1](a)

proc (x, y) options operator, arrow; y*exp(y*x) end proc

(2)

D[2](a)

proc (x, y) options operator, arrow; x*exp(y*x) end proc

(3)

retart

retart

(4)

"with(Units[Simple]):"

a := proc (x, y) options operator, arrow; exp(x*y) end proc

proc (x, y) options operator, arrow; Units:-Simple:-exp(Units:-Simple:-`*`(x, y)) end proc

(5)

D[1](a)

Error, (in tools/gensym) too many levels of recursion

 

D[2](a)

Error, (in tools/gensym) too many levels of recursion

 

NULL

Download problem.mw

Hi! I've got a set of subsets S with Abeing the i-subset. I need to sum up all the x's in all the Ai's. How can this be achieved?

(ex):

sum( { {a, b, c}, {a, b, d} } )= 2a+2b+c+d

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