Create a symbolic orthonormal matrix in mathematica - wolfram-mathematica

I need to create a 3 by 3 real orthonormal symbolic matrix in Mathematica.
How can I do so?

Not that I recommend this, but...
m = Array[a, {3, 3}];
{q, r} = QRDecomposition[m];
q2 = Simplify[q /. Conjugate -> Identity]
So q2 is a symbolic orthogonal matrix (assuming we work over reals).

You seem to want some SO(3) group parametrization in Mathematica I think. You will only have 3 independent symbols (variables), since you have 6 constraints from mutual orthogonality of vectors and the norms equal to 1. One way is to construct independent rotations around the 3 axes, and multiply those matrices. Here is the (perhaps too complex) code to do that:
makeOrthogonalMatrix[p_Symbol, q_Symbol, t_Symbol] :=
Module[{permute, matrixGeneratingFunctions},
permute = Function[perm, Permute[Transpose[Permute[#, perm]], perm] &];
matrixGeneratingFunctions =
Function /# FoldList[
permute[#2][#1] &,
{{Cos[#], 0, Sin[#]}, {0, 1, 0}, {-Sin[#], 0, Cos[#]}},
{{2, 1, 3}, {3, 2, 1}}];
#1.#2.#3 & ## MapThread[Compose, {matrixGeneratingFunctions, {p, q, t}}]];
Here is how this works:
In[62]:= makeOrthogonalMatrix[x,y,z]
Out[62]=
{{Cos[x] Cos[z]+Sin[x] Sin[y] Sin[z],Cos[z] Sin[x] Sin[y]-Cos[x] Sin[z],Cos[y] Sin[x]},
{Cos[y] Sin[z],Cos[y] Cos[z],-Sin[y]},
{-Cos[z] Sin[x]+Cos[x] Sin[y] Sin[z],Cos[x] Cos[z] Sin[y]+Sin[x] Sin[z],Cos[x] Cos[y]}}
You can check that the matrix is orthonormal, by using Simplify over the various column (or row) dot products.

I have found a "direct" way to impose special orthogonality.
See below.
(*DEFINITION OF ORTHOGONALITY AND SELF ADJUNCTNESS CONDITIONS:*)
MinorMatrix[m_List?MatrixQ] := Map[Reverse, Minors[m], {0, 1}]
CofactorMatrix[m_List?MatrixQ] := MapIndexed[#1 (-1)^(Plus ## #2) &, MinorMatrix[m], {2}]
UpperTriangle[ m_List?MatrixQ] := {m[[1, 1 ;; 3]], {0, m[[2, 2]], m[[2, 3]]}, {0, 0, m[[3, 3]]}};
FlatUpperTriangle[m_List?MatrixQ] := Flatten[{m[[1, 1 ;; 3]], m[[2, 2 ;; 3]], m[[3, 3]]}];
Orthogonalityconditions[m_List?MatrixQ] := Thread[FlatUpperTriangle[m.Transpose[m]] == FlatUpperTriangle[IdentityMatrix[3]]];
Selfadjunctconditions[m_List?MatrixQ] := Thread[FlatUpperTriangle[CofactorMatrix[m]] == FlatUpperTriangle[Transpose[m]]];
SO3conditions[m_List?MatrixQ] := Flatten[{Selfadjunctconditions[m], Orthogonalityconditions[m]}];
(*Building of an SO(3) matrix*)
mat = Table[Subscript[m, i, j], {i, 3}, {j, 3}];
$Assumptions = SO3conditions[mat]
Then
Simplify[Det[mat]]
gives 1;...and
MatrixForm[Simplify[mat.Transpose[mat]]
gives the identity matrix;
...finally
MatrixForm[Simplify[CofactorMatrix[mat] - Transpose[mat]]]
gives a Zero matrix.
========================================================================
This is what I was looking for when I asked my question!
However, let me know your thought on this method.
Marcellus

Marcellus, you have to use some parametrization of SO(3), since your general matrix has to reflect the RP3 topology of the group. No single parametrization will cover the whole group without either multivaluedness or singular points. Wikipedia has a nice page about the various charts on SO(3).
Maybe one of the conceptually simplest is the exponential map from the Lie algebra so(3).
Define an antisymmetric, real A (which spans so(3))
A = {{0, a, -c},
{-a, 0, b},
{c, -b, 0}};
Then MatrixExp[A] is an element of SO(3).
We can check that this is so, using
Transpose[MatrixExp[A]].MatrixExp[A] == IdentityMatrix[3] // Simplify
If we write t^2 = a^2 + b^2 + c^2, we can simplify the matrix exponential down to
{{ b^2 + (a^2 + c^2) Cos[t] , b c (1 - Cos[t]) + a t Sin[t], a b (1 - Cos[t]) - c t Sin[t]},
{b c (1 - Cos[t]) - a t Sin[t], c^2 + (a^2 + b^2) Cos[t] , a c (1 - Cos[t]) + b t Sin[t]},
{a b (1 - Cos[t]) + c t Sin[t], a c (1 - Cos[t]) - b t Sin[t], a^2 + (b^2 + c^2) Cos[t]}} / t^2
Note that this is basically the same parametrization as RotationMatrix gives.
Compare with the output from
RotationMatrix[s, {b, c, a}] // ComplexExpand // Simplify[#, Trig -> False] &;
% /. a^2 + b^2 + c^2 -> 1

Although I really like the idea of Marcellus' answer to his own question, it's not completely correct. Unfortunately, the conditions he arrives at also result in
Simplify[Transpose[mat] - mat]
evaluating to a zero matrix! This is clearly not right. Here's an approach that's both correct and more direct:
OrthogonalityConditions[m_List?MatrixQ] := Thread[Flatten[m.Transpose[m]] == Flatten[IdentityMatrix[3]]];
SO3Conditions[m_List?MatrixQ] := Flatten[{OrthogonalityConditions[m], Det[m] == 1}];
i.e. multiplying a rotation matrix by its transpose results in the identity matrix, and the determinant of a rotation matrix is 1.

Related

How do I take derivatives in Wolfram Mathematica?

I am trying to learn a little bit about Wolfram Mathematica.
I want to define a symbolic function
where x is a vector, g is a function that takes a vector and returns a vector and h is a function that takes a vector and returns a scalar.
I don't want to commit to specific g and h, I just want to have a symbolic representation for them.
I would like to get a symbolic form for the third order derivatives (which would be a tensor) -- is there a way to do that in Wolfram Mathematica?
EDIT: I should mention, A and C are matrices, and b and d are vectors.
Here is what I tried and didn't work:
Try this
f[x_] := x*E^x
and then this
f'[x]
returns this
E^x + E^x x
and this
f''[x]
returns this
2 E^x + E^x x
Three methods of notation, all producing the same result.
f[x_] := Sin[x] + x^2
D[f[x], x]
2 x + Cos[x]
f'[x]
2 x + Cos[x]
f''[x]
2 - Sin[x]
using an alternative form of definition of f
Clear[f]
f = Sin[x] + x^2
D[f, x]
2 x + Cos[x]
δx f
2 x + Cos[x]
δ{x,2} f
2 - Sin[x]
Note
δ{x,2} f is supposed to be the subscript form of D[f, {x, 2}] but web formatting is limited.
Scoping out matrix and vector dimensions, and using S instead of C since the latter is a protected (uppercase) symbol.
A = {{1, 2, 3}, {4, 5, 6}};
x = {2, 4, 8};
A.x
{34, 76}
b = {3, 5};
h = 3;
h (A.x + b)
{111, 243}
S = {{1, 2}, {3, 4}, {5, 6}};
S.(h (A.x + b))
{597, 1305, 2013}
d = {2, 4, 8};
g = 2;
g (S.(h (A.x + b)) + d)
{1198, 2618, 4042}
So compatible matrix and vector assumptions can be made. (It turns out the derivative result comes out the same without taking the trouble to make the assumptions.)
Clear[A, x, b, S, d]
$Assumptions = {
Element[A, Matrices[{m, n}]],
Element[x, Vectors[n]],
Element[b, Vectors[m]],
Element[S, Matrices[{n, m}]],
Element[d, Vectors[n]]};
f = g (S.(h (A.x + b)) + d);
D[f, x]
g S.(h A.1)
D[f, {x, 3}]
0
I'm not sure if these results are correct so if you find out do comment.

Constructing a triadiagonal matrix in Mathematica where nonzero elements contain functions and variables

Suppose I want to construct a matrix A such that A[[i,i]]=f[x_,y_]+d[i], A[[i,i+1]]=u[i], A[[i+1,i]]=l[i], i=1,N . Say, f[x_,y_]=x^2+y^2.
How can I code this in Mathematica?
Additionally, if I want to integrate the first diagonal element of A, i.e. A[[1,1]] over x and y, both running from 0 to 1, how can I do that?
In[1]:= n = 4;
f[x_, y_] := x^2 + y^2;
A = Normal[SparseArray[{
{i_,i_}/;i>1 -> f[x,y]+ d[i],
{i_,j_}/;j-i==1 -> u[i],
{i_,j_}/;i-j==1 -> l[i-1],
{1, 1} -> Integrate[f[x,y]+d[1], {x,0,1}, {y,0,1}]},
{n, n}]]
Out[3]= {{2/3+d[1], l[1], 0, 0},
{u[1], x^2+y^2+ d[2], l[2], 0},
{0, u[2], x^2+y^2+d[3], l[3]},
{0, 0, u[3], x^2+y^2+d[4]}}
Band is tailored specifically for this:
myTridiagonalMatrix#n_Integer?Positive :=
SparseArray[
{ Band#{1, 1} -> f[x, y] + Array[d, n]
, Band#{1, 2} -> Array[u, n - 1]
, Band#{2, 1} -> Array[l, n - 1]}
, {n, n}]
Check it out (no need to define f, d, u, l):
myTridiagonalMatrix#5 // MatrixForm
Note that MatrixForm should not be part of a definition. For example, it's a bad idea to set A = (something) // MatrixForm. You will get a MatrixForm object instead of a table (= array of arrays) or a sparse array, and its only purpose is to be pretty-printed in FrontEnd. Trying to use MatrixForm in calculations will yield errors and will lead to unnecessary confusion.
Integrating the element at {1, 1}:
myTridiagonalMatrixWithFirstDiagonalElementIntegrated#n_Integer?Positive :=
MapAt[
Integrate[#, {x, 0, 1}, {y, 0, 1}]&
, myTridiagonalMatrix#n
, {1, 1}]
You may check it out without defining f or d, as well:
myTridiagonalMatrixWithFirstDiagonalElementIntegrated#5
The latter operation, however, looks suspicious. For example, it does not leave your matrix (or its corresponding linear system) invariant w.r.t. reasonable transformations. (This operation does not even preserve linearity of matrices.) You probably don't want to do it.
Comment on comment above: there's no need to define A[x_, y_] := … to Integrate[A[[1,1]], {x,0,1}, {y,0,1}]. Note that A[[1,1]] is totally different from A[1, 1]: the former is Part[A, 1, 1] which is a certain element of table A. A[1, 1] is a different expression: if A is some table then A[1, 1] is (that table)[1, 1], which is a valid expression but is normally considered meaningless.

how to generate a plot of planar Cantor set in mathematica

I am wondering if anyone can help me to plot the Cantor dust on the plane in Mathematica. This is linked to the Cantor set.
Thanks a lot.
EDIT
I actually wanted to have something like this:
Here's a naive and probably not very optimized way of reproducing the graphics for the ternary Cantor set construction:
cantorRule = Line[{{a_, n_}, {b_, n_}}] :>
With[{d = b - a, np = n - .1},
{Line[{{a, np}, {a + d/3, np}}], Line[{{b - d/3, np}, {b, np}}]}]
Graphics[{CapForm["Butt"], Thickness[.05],
Flatten#NestList[#/.cantorRule&, Line[{{0., 0}, {1., 0}}], 6]}]
To make Cantor dust using the same replacement rules, we take the result at a particular level, e.g. 4:
dust4=Flatten#Nest[#/.cantorRule&,Line[{{0.,0},{1.,0}}],4]/.Line[{{a_,_},{b_,_}}]:>{a,b}
and take tuples of it
dust4 = Transpose /# Tuples[dust4, 2];
Then we just plot the rectangles
Graphics[Rectangle ### dust4]
Edit: Cantor dust + squares
Changed specs -> New, but similar, solution (still not optimized).
Set n to be a positive integer and choice any subset of 1,...,n then
n = 3; choice = {1, 3};
CanDChoice = c:CanD[__]/;Length[c]===n :> CanD[c[[choice]]];
splitRange = {a_, b_} :> With[{d = (b - a + 0.)/n},
CanD##NestList[# + d &, {a, a + d}, n - 1]];
cantLevToRect[lev_]:=Rectangle###(Transpose/#Tuples[{lev}/.CanD->Sequence,2])
dust = NestList[# /. CanDChoice /. splitRange &, {0, 1}, 4] // Rest;
Graphics[{FaceForm[LightGray], EdgeForm[Black],
Table[cantLevToRect[lev], {lev, Most#dust}],
FaceForm[Black], cantLevToRect[Last#dust /. CanDChoice]}]
Here's the graphics for
n = 7; choice = {1, 2, 4, 6, 7};
dust = NestList[# /. CanDChoice /. splitRange &, {0, 1}, 2] // Rest;
and everything else the same:
Once can use the following approach. Define cantor function:
cantorF[r:(0|1)] = r;
cantorF[r_Rational /; 0 < r < 1] :=
Module[{digs, scale}, {digs, scale} = RealDigits[r, 3];
If[! FreeQ[digs, 1],
digs = Append[TakeWhile[Most[digs]~Join~Last[digs], # != 1 &], 1];];
FromDigits[{digs, scale}, 2]]
Then form the dust by computing differences of F[n/3^k]-F[(n+1/2)/3^k]:
With[{k = 4},
Outer[Times, #, #] &[
Table[(cantorF[(n + 1/2)/3^k] - cantorF[(n)/3^k]), {n, 0,
3^k - 1}]]] // ArrayPlot
I like recursive functions, so
cantor[size_, n_][pt_] :=
With[{s = size/3, ct = cantor[size/3, n - 1]},
{ct[pt], ct[pt + {2 s, 0}], ct[pt + {0, 2 s}], ct[pt + {2 s, 2 s}]}
]
cantor[size_, 0][pt_] := Rectangle[pt, pt + {size, size}]
drawCantor[n_] := Graphics[cantor[1, n][{0, 0}]]
drawCantor[5]
Explanation: size is the edge length of the square the set fits into. pt is the {x,y} coordinates of it lower left corner.

Summation up to a variable integer: How to get the coefficients?

This is an example. I want to know if there is a general way to deal with this kind of problems.
Suppose I have a function (a ε ℜ) :
f[a_, n_Integer, m_Integer] := Sum[a^i k[i],{i,0,n}]^m
And I need a closed form for the coefficient a^p. What is the better way to proceed?
Note 1:In this particular case, one could go manually trying to represent the sum through Multinomial[ ], but it seems difficult to write down the Multinomial terms for a variable number of arguments, and besides, I want Mma to do it.
Note 2: Of course
Collect[f[a, 3, 4], a]
Will do, but only for a given m and n.
Note 3: This question is related to this other one. My application is different, but probably the same methods apply. So, feel free to answer both with a single shot.
Note 4:
You can model the multinomial theorem with a function like:
f[n_, m_] :=
Sum[KroneckerDelta[m - Sum[r[i], {i, n}]]
(Multinomial ## Sequence#Array[r, n])
Product[x[i]^r[i], {i, n}],
Evaluate#(Sequence ## Table[{r[i], 0, m}, {i, 1, n}])];
So, for example
f[2,3]
is the cube of a binomial
x[1]^3+ 3 x[1]^2 x[2]+ 3 x[1] x[2]^2+ x[2]^3
The coefficient by a^k can be viewed as derivative of order k at zero divided by k!. In version 8, there is a function BellY, which allows to construct a derivative at a point for composition of functions, out of derivatives of individual components. Basically, for f[g[x]] and expanding around x==0 we find Derivative[p][Function[x,f[g[x]]][0] as
BellY[ Table[ { Derivative[k][f][g[0]], Derivative[k][g][0]}, {k, 1, p} ] ]/p!
This is also known as generalized Bell polynomial, see wiki.
In the case at hand:
f[a_, n_Integer, m_Integer] := Sum[a^i k[i], {i, 0, n}]^m
With[{n = 3, m = 4, p = 7},
BellY[ Table[{FactorialPower[m, s] k[0]^(m - s),
If[s <= n, s! k[s], 0]}, {s, 1, p}]]/p!] // Distribute
(*
Out[80]= 4 k[1] k[2]^3 + 12 k[1]^2 k[2] k[3] + 12 k[0] k[2]^2 k[3] +
12 k[0] k[1] k[3]^2
*)
With[{n = 3, m = 4, p = 7}, Coefficient[f[a, n, m], a, p]]
(*
Out[81]= 4 k[1] k[2]^3 + 12 k[1]^2 k[2] k[3] + 12 k[0] k[2]^2 k[3] +
12 k[0] k[1] k[3]^2
*)
Doing it this way is more computationally efficient than building the entire expression and extracting coefficients.
EDIT The approach here outlined will work for symbolic orders n and m, but requires explicit value for p. When using it is this circumstances, it is better to replace If with its Piecewise analog, e.g. Boole:
With[{p = 2},
BellY[Table[{FactorialPower[m, s] k[0]^(m - s),
Boole[s <= n] s! k[s]}, {s, 1, p}]]/p!]
(* 1/2 (Boole[1 <= n]^2 FactorialPower[m, 2] k[0]^(-2 + m)
k[1]^2 + 2 m Boole[2 <= n] k[0]^(-1 + m) k[2]) *)

Mathematica integral with many singularities

What's the best way of getting Mathematica 7 or 8 to do the integral
NIntegrate[Exp[-x]/Sin[Pi x], {x, 0, 50}]
There are poles at every integer - and we want the Cauchy principle value.
The idea is to get a good approximation for the integral from 0 to infinity.
With Integrate there is the option PrincipleValue -> True.
With NIntegrate I can give it the option Exclusions -> (Sin[Pi x] == 0), or manually give it the poles by
NIntegrate[Exp[-x]/Sin[Pi x], Evaluate[{x, 0, Sequence##Range[50], 50}]]
The original command and the above two NIntegrate tricks give the result 60980 +/- 10. But they all spit out errors. What is the best way of getting a quick reliable result for this integral without Mathematica wanting to give errors?
Simon, is there reason to believe your integral is convergent ?
In[52]:= f[k_Integer, eps_Real] :=
NIntegrate[Exp[-x]/Sin[Pi x], {x, k + eps, k + 1 - eps}]
In[53]:= Sum[f[k, 1.0*10^-4], {k, 0, 50}]
Out[53]= 2.72613
In[54]:= Sum[f[k, 1.0*10^-5], {k, 0, 50}]
Out[54]= 3.45906
In[55]:= Sum[f[k, 1.0*10^-6], {k, 0, 50}]
Out[55]= 4.19199
It looks like the problem is at x==0. Splitting integrand k+eps to k+1-eps for integer values of k:
In[65]:= int =
Sum[(-1)^k Exp[-k ], {k, 0, Infinity}] Integrate[
Exp[-x]/Sin[Pi x], {x, eps, 1 - eps}, Assumptions -> 0 < eps < 1/2]
Out[65]= (1/((1 +
E) (I + \[Pi])))E (2 E^(-1 + eps - I eps \[Pi])
Hypergeometric2F1[1, (I + \[Pi])/(2 \[Pi]), 3/2 + I/(2 \[Pi]),
E^(-2 I eps \[Pi])] +
2 E^(I eps (I + \[Pi]))
Hypergeometric2F1[1, (I + \[Pi])/(2 \[Pi]), 3/2 + I/(2 \[Pi]),
E^(2 I eps \[Pi])])
In[73]:= N[int /. eps -> 10^-6, 20]
Out[73]= 4.1919897038160855098 + 0.*10^-20 I
In[74]:= N[int /. eps -> 10^-4, 20]
Out[74]= 2.7261330651934049862 + 0.*10^-20 I
In[75]:= N[int /. eps -> 10^-5, 20]
Out[75]= 3.4590554287709991277 + 0.*10^-20 I
As you see there is a logarithmic singularity.
In[79]:= ser =
Assuming[0 < eps < 1/32, FullSimplify[Series[int, {eps, 0, 1}]]]
Out[79]= SeriesData[eps, 0, {(I*(-1 + E)*Pi -
2*(1 + E)*HarmonicNumber[-(-I + Pi)/(2*Pi)] +
Log[1/(4*eps^2*Pi^2)] - 2*E*Log[2*eps*Pi])/(2*(1 + E)*Pi),
(-1 + E)/((1 + E)*Pi)}, 0, 2, 1]
In[80]:= Normal[
ser] /. {{eps -> 1.*^-6}, {eps -> 0.00001}, {eps -> 0.0001}}
Out[80]= {4.191989703816426 - 7.603403526913691*^-17*I,
3.459055428805136 -
7.603403526913691*^-17*I,
2.726133068607085 - 7.603403526913691*^-17*I}
EDIT
Out[79] of the code above gives the series expansion for eps->0, and if these two logarithmic terms get combined, we get
In[7]:= ser = SeriesData[eps, 0,
{(I*(-1 + E)*Pi - 2*(1 + E)*HarmonicNumber[-(-I + Pi)/(2*Pi)] +
Log[1/(4*eps^2*Pi^2)] - 2*E*Log[2*eps*Pi])/(2*(1 + E)*
Pi),
(-1 + E)/((1 + E)*Pi)}, 0, 2, 1];
In[8]:= Collect[Normal[PowerExpand //# (ser + O[eps])],
Log[eps], FullSimplify]
Out[8]= -(Log[eps]/\[Pi]) + (
I (-1 + E) \[Pi] -
2 (1 + E) (HarmonicNumber[-((-I + \[Pi])/(2 \[Pi]))] +
Log[2 \[Pi]]))/(2 (1 + E) \[Pi])
Clearly the -Log[eps]/Pi came from the pole at x==0. So if one subtracts this, just like principle value method does this for other poles you end up with a finitely value:
In[9]:= % /. Log[eps] -> 0
Out[9]= (I (-1 + E) \[Pi] -
2 (1 + E) (HarmonicNumber[-((-I + \[Pi])/(2 \[Pi]))] +
Log[2 \[Pi]]))/(2 (1 + E) \[Pi])
In[10]:= N[%, 20]
Out[10]= -0.20562403655659928968 + 0.*10^-21 I
Of course, this result is difficult to verify numerically, but you might know more that I do about your problem.
EDIT 2
This edit is to justify In[65] input that computes the original regularized integral. We are computing
Sum[ Integrate[ Exp[-x]/Sin[Pi*x], {x, k+eps, k+1-eps}], {k, 0, Infinity}] ==
Sum[ Integrate[ Exp[-x-k]/Sin[Pi*(k+x)], {x, eps, 1-eps}], {k, 0, Infinity}] ==
Sum[ (-1)^k*Exp[-k]*Integrate[ Exp[-x]/Sin[Pi*x], {x, eps, 1-eps}],
{k, 0, Infinity}] ==
Sum[ (-1)^k*Exp[-k], {k, 0, Infinity}] *
Integrate[ Exp[-x]/Sin[Pi*x], {x, eps, 1-eps}]
In the third line Sin[Pi*(k+x)] == (-1)^k*Sin[Pi*x] for integer k was used.
Simon, I haven't spent much time with your integral, but you should try looking at stationary phase approximation. What you have is a smooth function (exp), and a highly oscillatory function (sine). The work involved is now in brow-beating the 1/sin(x) into the form exp(if(x))
Alternatively, you could use the series expansion of the cosecant (not valid at poles):
In[1]:=Series[Csc[x], {x, 0, 5}]
(formatted) Out[1]=1/x + x/6 + 7/360 x^3 + 31/15120 x^5 +O[x]^6
Note that for all m>-1, you have the following:
In[2]:=Integrate[x^m Exp[-x], {x, 0, Infinity}, Assumptions -> m > -1]
Out[2]=Gamma[1+m]
However, summing the series with the coefficients of cosecant (from wikipedia), not including 1/x Exp[-x] case, which doesn't converge on [0,Infinity].
c[m_] := (-1)^(m + 1) 2 (2^(2 m - 1) - 1) BernoulliB[2 m]/Factorial[2 m];
Sum[c[m] Gamma[1 + 2 m - 1], {m, 1, Infinity}]
does not converge either...
So, I'm not sure that you can work out an approximation for the integral to infinity, but I if you're satisfied with a solution upto some large N, I hope these help.
I have to agree with Sasha, the integral does not appear to be convergent. However, if you exclude x == 0 and break the integral into pieces
Integrate[Exp[-x]/Sin[Pi x], {x, n + 1/2, n + 3/2}, PrincipalValue -> True]
where n >= 0 && Element[n, Integers], then it seems you may get an alternating series
I Sum[ (-1/E)^n, {n, 1, Infinity}] == - I / (1 + E )
Now, I only took it out to n == 4, but it looks reasonable. However, for the integral above with Assumptions -> Element[n, Integers] && n >= 0 Mathematica gives
If[ 2 n >= 1, - I / E, Integrate[ ... ] ]
which just doesn't conform to the individual cases. As an additional note, if the pole lies at the boundary of the integration region, i.e. your limits are {x, n, n + 1}, you only get DirectedInfinitys. A quick look at the plot implies that you with the limits {x, n, n + 1} you only have a strictly positive or negative integrand, so the infinite value may be due to the lack of compensation which {x, n + 1/2, n + 3/2} gives you. Checking with {x, n, n + 2}, however it only spits out the unevaluated integral.

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