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Sage Interactions - Linear Algebra

Contents

Sage Interactions - Linear Algebra

Numerical instability of the classical Gram-Schmidt algorithm

by Marshall Hampton

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def GS_classic(a_list):
    '''
    Given a list of vectors or a matrix, returns the QR factorization using the classical (and numerically unstable) Gram-Schmidt algorithm.    
    '''
    if type(a_list) != list:
        cols = a_list.cols()
        a_list = [x for x in cols]
    indices = range(len(a_list))
    q = []
    r = [[0 for i in indices] for j in indices]
    v = [a_list[i][:] for i in indices]
    for i in indices:
        for j in range(0,i):
            r[j][i] = q[j].inner_product(a_list[i])
            v[i] = v[i] - r[j][i]*q[j]
        r[i][i] = (v[i]*v[i])**(1/2)
        q.append(v[i]/r[i][i])
    q = matrix([q[i] for i in indices]).transpose()
    return q, matrix(r)
def GS_modern(a_list):
    '''
    Given a list of vectors or a matrix, returns the QR factorization using the 'modern' Gram-Schmidt algorithm.    
    '''
    if type(a_list) != list:
        cols = a_list.cols()
        a_list = [x for x in cols]
    indices = range(len(a_list))
    q = []
    r = [[0 for i in indices] for j in indices]
    v = [a_list[i][:] for i in indices]
    for i in indices:
        r[i][i] = v[i].norm(2)
        q.append(v[i]/r[i][i])
        for j in range(i+1, len(indices)):
            r[i][j] = q[i].inner_product(v[j])
            v[j] = v[j] - r[i][j]*q[i]
    q = matrix([q[i] for i in indices]).transpose()
    return q, matrix(r)
pretty_print(html('<h2>Numerical instability of the classical Gram-Schmidt algorithm</h2>'))
@interact
def gstest(precision = slider(range(3,53), default = 10), a1 = input_box([1,1/1000,1/1000]), a2 = input_box([1,1/1000,0]), a3 = input_box([1,0,1/1000])):
    myR = RealField(precision)
    displayR = RealField(5)
    pretty_print(html('precision in bits: ' + str(precision) + '<br>'))
    A = matrix([a1,a2,a3])
    A = [vector(myR,x) for x in A]
    qn, rn = GS_classic(A)
    qb, rb = GS_modern(A)
    pretty_print(html('Classical Gram-Schmidt:'))
    show(matrix(displayR,qn))
    pretty_print(html('Stable Gram-Schmidt:'))
    show(matrix(displayR,qb))

Equality of det(A) and det(A.tranpose())

by Marshall Hampton

xxxxxxxxxx
 
srg = srange(-4,4,1/10,include_endpoint=True)
@interact
def dualv(a1=slider(srg,default=1),a2=slider(srg,default=2), a3=slider(srg,default=-1),a4=slider(srg,default=3)):
    A1 = arrow2d([0,0],[a1,a2],rgbcolor='black')
    A2 = arrow2d([0,0],[a3,a4],rgbcolor='black')
    A3 = arrow2d([0,0],[a1,a3],rgbcolor='black')
    A4 = arrow2d([0,0],[a2,a4],rgbcolor='black')
    p1 = polygon([[0,0],[a1,a2],[a1+a3,a2+a4],[a3,a4],[0,0]], alpha=.5)
    p2 = polygon([[0,0],[a1,a3],[a1+a2,a3+a4],[a2,a4],[0,0]],rgbcolor='red', alpha=.5)
    A = matrix([[a1,a2],[a3,a4]])
    pretty_print(html('<h3>The determinant of a matrix is equal to the determinant of the transpose</h3>'))
    pretty_print(html("$\det(%s) = \det(%s)=%s$"%(latex(A),latex(A.transpose()),latex(RR(A.determinant())))))
    show(A1+A2+A3+A4+p1+p2)

Linear transformations

by Jason Grout

A square matrix defines a linear transformation which rotates and/or scales vectors. In the interact command below, the red vector represents the original vector (v) and the blue vector represents the image w under the linear transformation. You can change the angle and length of v by changing theta and r.

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@interact
def linear_transformation(A=matrix([[1,-1],[-1,1/2]]),theta=slider(0, 2*pi, .1), r=slider(0.1, 2, .1, default=1)):
    v=vector([r*cos(theta), r*sin(theta)])
    w = A*v
    circles = sum([circle((0,0), radius=i, color='black') for i in [1..2]])
    pretty_print(html("$%s %s=%s$"%tuple(map(latex, [A, v.column().n(4), w.column().n(4)]))))
    show(v.plot(color='red')+w.plot(color='blue')+circles,aspect_ratio=1)

Gerschgorin Circle Theorem

by Marshall Hampton. This animated version requires convert (imagemagick) to be installed, but it can easily be modified to a static version. The animation illustrates the idea behind the stronger version of Gerschgorin's theorem, which says that if the disks around the eigenvalues are disjoint then there is one eigenvalue per disk. The proof is by continuity of the eigenvalues under a homotopy to a diagonal matrix.

xxxxxxxxxx
 
from scipy import linalg
pretty_print(html('<h2>The Gerschgorin circle theorem</h2>'))
@interact
def Gerschgorin(Ain = input_box(default='[[10,1,1/10,0],[-1,9,0,1],[1,0,2,3/10],[-.5,0,-.3,1]]', type = str, label = 'A = '), an_size = slider(1,100,1,1.0)):
    A = sage_eval(Ain)
    size = len(A)
    pretty_print(html('$A = ' + latex(matrix(RealField(10),A))+'$'))
    A = matrix(RealField(10),A)
    B = [[0 for i in range(size)] for j in range(size)]
    for i in range(size):
        B[i][i] = A[i][i]
    B = matrix(B)
    frames = []
​
    centers = [(real(q),imag(q)) for q in [A[i][i] for i in range(size)]]
    radii_row = [sum([abs(A[i][j]) for j in range(i)+range(i+1,size)]) for i in range(size)]
    radii_col = [sum([abs(A[j][i]) for j in range(i)+range(i+1,size)]) for i in range(size)]
    x_min = min([centers[i][0]-radii_row[i] for i in range(size)]+[centers[i][0]-radii_col[i] for i in range(size)])
    x_max = max([centers[i][0]+radii_row[i] for i in range(size)]+[centers[i][0]+radii_col[i] for i in range(size)])
    y_min = min([centers[i][1]-radii_row[i] for i in range(size)]+[centers[i][1]-radii_col[i] for i in range(size)])
    y_max = max([centers[i][1]+radii_row[i] for i in range(size)]+[centers[i][1]+radii_col[i] for i in range(size)])
​
    if an_size > 1: 
        t_range= srange(0,1+1/an_size,1/an_size)
    else:
        t_range = [1]
    for t in t_range:
        C = t*A + (1-t)*B
        eigs = [CDF(x) for x in linalg.eigvals(C.numpy())]
        eigpoints = points([(real(q),imag(q)) for q in eigs],pointsize = 10, rgbcolor = (0,0,0))
        centers = [(real(q),imag(q)) for q in [A[i][i] for i in range(size)]]
        radii_row = [sum([abs(C[i][j]) for j in range(i)+range(i+1,size)]) for i in range(size)]
        radii_col = [sum([abs(C[j][i]) for j in range(i)+range(i+1,size)]) for i in range(size)]
        scale = max([(x_max-x_min),(y_max-y_min)])
        scale = 7/scale
        row_circles = sum([circle(centers[i],radii_row[i],fill=True, alpha = .3) for i in range(size)])
        col_circles = sum([circle(centers[i],radii_col[i],fill=True, rgbcolor = (1,0,0), alpha = .3) for i in range(size)])
        ft = eigpoints+row_circles+col_circles
        frames.append(ft)
    show(animate(frames,figsize = [(x_max-x_min)*scale,(y_max-y_min)*scale], xmin = x_min, xmax=x_max, ymin = y_min, ymax = y_max))

Singular value decomposition

by Marshall Hampton

xxxxxxxxxx
 
import scipy.linalg as lin
var('t')
def rotell(sig,umat,t,offset=0):
    temp = matrix(umat)*matrix(2,1,[sig[0]*cos(t),sig[1]*sin(t)])
    return [offset+temp[0][0],temp[1][0]]
@interact
def svd_vis(a11=slider(-1,1,.05,1),a12=slider(-1,1,.05,1),a21=slider(-1,1,.05,0),a22=slider(-1,1,.05,1),ofs= ('offset image from domain',False)):
    rf_low = RealField(12)
    my_mat = matrix(rf_low,2,2,[a11,a12,a21,a22])
    u,s,vh = lin.svd(my_mat.numpy())
    if ofs: 
        offset = 3
        fsize = 6
        colors = [(1,0,0),(0,0,1),(1,0,0),(0,0,1)]
    else: 
        offset = 0
        fsize = 5
        colors = [(1,0,0),(0,0,1),(.7,.2,0),(0,.3,.7)]
    vvects = sum([arrow([0,0],matrix(vh).row(i),rgbcolor = colors[i]) for i in (0,1)])    
    uvects = Graphics()
    for i in (0,1):
        if s[i] != 0: uvects += arrow([offset,0],vector([offset,0])+matrix(s*u).column(i),rgbcolor = colors[i+2])
    pretty_print(html('<h3>Singular value decomposition: image of the unit circle and the singular vectors</h3>'))
    pretty_print(html("$A = %s  = %s %s %s$"%(latex(my_mat), latex(matrix(rf_low,u.tolist())), latex(matrix(rf_low,2,2,[s[0],0,0,s[1]])), latex(matrix(rf_low,vh.tolist())))))
    image_ell = parametric_plot(rotell(s,u,t, offset),(0,2*pi))
    graph_stuff=circle((0,0),1)+image_ell+vvects+uvects
    graph_stuff.set_aspect_ratio(1)
    show(graph_stuff,frame = False,axes=False,figsize=[fsize,fsize])

Discrete Fourier Transform

by Marshall Hampton

xxxxxxxxxx
 
import scipy.fftpack as Fourier
@interact
def discrete_fourier(f = input_box(default=sum([sin(k*x) for k in range(1,5,2)])), scale = slider(.1,20,.1,5)):
    pbegin = -float(pi)*scale
    pend = float(pi)*scale
    pretty_print(html("<h3>Function plot and its discrete Fourier transform</h3>"))
    show(plot(f, (x,pbegin, pend), plot_points = 512), figsize = [4,3])
    f_vals = [f(x=ind) for ind in srange(pbegin, pend,(pend-pbegin)/512.0)]
    my_fft = Fourier.fft(f_vals)
    show(list_plot([abs(i) for i in my_fft], plotjoined=True), figsize = [4,3])

The Gauss-Jordan method for inverting a matrix

by Hristo Inouzhe

xxxxxxxxxx
 
#Choose the size D of the square matrix:
D = 3
​
example = [[1 if k==j else 0 for k in range(D)] for j in range(D)]
example[0][-1] = 2
example[-1][0] = 3
​
@interact
def _(M=input_grid(D,D, default = example,
                   label='Matrix to invert', to_value=matrix),
      tt = text_control('Enter the bits of precision used'
                        ' (only if you entered floating point numbers)'),  
      precision = slider(5,100,5,20),
      auto_update=False):
    if det(M)==0:
        print 'Failure: Matrix is not invertible'
        return
    if M.base_ring() == RR:
        M = M.apply_map(RealField(precision))
    N=M
    M=M.augment(identity_matrix(D))
    print 'We construct the augmented matrix'
    show(M)
    for m in range(0,D-1):
        if M[m,m] == 0:
            lista = [(abs(M[j,m]),j) for j in range(m+1,D)]
            maxi, c = max(lista)
            M[c,:],M[m,:]=M[m,:],M[c,:]
            print 'We permute rows %d and %d'%(m+1,c+1)
            show(M)
        for n in range(m+1,D):
            a=M[m,m]
            if M[n,m]!=0:
                print "We add %s times row %d to row %d"%(-M[n,m]/a, m+1, n+1)
                M=M.with_added_multiple_of_row(n,m,-M[n,m]/a)
                show(M)
    for m in range(D-1,-1,-1):
        for n in range(m-1,-1,-1):
            a=M[m,m]
            if M[n,m]!=0:
                print "We add %s times row %d to the row %d"%(-M[n,m]/a, m+1, n+1)
                M=M.with_added_multiple_of_row(n,m,-M[n,m]/a)
                show(M)       
    for m in range(0,D):
        if M[m,m]!=1:
            print 'We divide row %d by %s'%(m+1,M[m,m])
            M = M.with_row_set_to_multiple_of_row(m,m,1/M[m,m])
            show(M)   
    M=M.submatrix(0,D,D)
    print 'We keep the right submatrix, which contains the inverse'        
    html('$$M^{-1}=%s$$'%latex(M))
    print 'We check it actually is the inverse'
    html('$$M^{-1}*M=%s*%s=%s$$'%(latex(M),latex(N),latex(M*N)))

...(goes all the way to invert the matrix)

Solution of an homogeneous system of linear equations

by Pablo Angulo

Coefficients are introduced as a matrix in a single text box. The number of equations and unknowns are arbitrary.

xxxxxxxxxx
 
from sage.misc.html import HtmlFragment
​
def HSLE_as_latex(A, variables):
    nvars = A.ncols()
    pretty_print(HtmlFragment( 
    r'$$\left\{\begin{array}{%s}'%('r'*(nvars+1))+
    r'\\'.join('%s=&0'%( 
        ' & '.join((r'%s%s\cdot %s'%('+' if c>0 else '',c,v) if c else '') for c,v in zip(row, variables))
        if not row.is_zero() else '&'*(nvars-1)+'0' 
               ) for row in A)+
    r'\end{array}\right.$$'))
​
@interact
def SEL(A='[(0,1,-1,2),(-1,0,2,4), (0,-1,1,-2)]',
        auto_update=False
    ):
    M = A = matrix(eval(A))
    neqs = M.nrows()
    nvars = M.ncols()
    var_names = ','.join('x%d'%j for j in [1..nvars])
    variables = var(var_names)
​
    HSLE_as_latex(M, variables)
    pretty_print(HtmlFragment( 'SEL in matrix form'))
    show(M)
​
    pivot = {}
    ibound_variables = []
    for m,row in enumerate(M):
        for k in range(m,nvars):
            lista = [(abs(M[j,k]),j) for j in range(m,neqs)]
            maxi, c = max(lista)
            if maxi > 0:
                ibound_variables.append(k)
                if M[m,k]==0:
                    M[c,:],M[m,:]=M[m,:],M[c,:]
                    pretty_print( HtmlFragment('We permute rows %d and %d'%(m+1,c+1)))
                    show(M)
                pivot[m] = k
                break
​
        a=M[m,k]
        for n in range(m+1,neqs):
            if M[n,k]!=0:
                pretty_print( HtmlFragment("We add %s times row %d to row %d"%(-M[n,k]/a, m+1, n+1)))
                M=M.with_added_multiple_of_row(n,m,-M[n,k]/a)
                show(M)
​
    pretty_print( HtmlFragment('Equivalent system of equations'))
    HSLE_as_latex(M, variables)
    SEL_type = 'compatible'
    null_rows = None
    for k,row in enumerate(M):
        if row.is_zero():
            pretty_print( HtmlFragment('We remove trivial 0=0 equations'))
            M = M[:k,:]
            HSLE_as_latex(M, variables)
                
    ifree_variables = [k for k in range(nvars) if k not in ibound_variables]
    bound_variables = [variables[k] for k in ibound_variables]
    free_variables = [variables[k] for k in ifree_variables]
    pretty_print( HtmlFragment('Free variables: %s'% free_variables))
    pretty_print( HtmlFragment('Bound variables: %s'% bound_variables))
    reduced_eqs = [
        sum(c*v for c,v in zip(row, variables))==0
        for row in M
    ]
    xvector = vector(variables)
    if len(bound_variables)==1:
        soldict = solve(reduced_eqs, bound_variables[0], solution_dict=True)[0]
    else:
        soldict = solve(reduced_eqs, bound_variables, solution_dict=True)[0]
​
    pretty_print( HtmlFragment('Solution in parametric form'))
    parametric_sol = matrix(
        xvector.apply_map(lambda s:s.subs(soldict))
    ).transpose()
    show(parametric_sol)
    pretty_print( HtmlFragment('Generators'))
    pretty_print( HtmlFragment(
        r'$$\langle %s\rangle$$'%','.join(latex(
            parametric_sol.subs(dict((variables[k],1 if j==k else 0) for k in ifree_variables))
        ) for j in ifree_variables)
    ))
    pretty_print( HtmlFragment('Dimension is %d'%len(free_variables)))

-  ⇤ ← Revision 1 as of 2008-05-07 13:28:12 → 
  Size: 5314
  Editor: schilly
  Comment:
+   ← Revision 32 as of 2019-10-09 21:28:27 → ⇥
  Size: 14939
  Editor: pang
  Comment: attachment:HSEL_1.png
-Deletions are marked like this.
+Additions are marked like this.
 Line 2:
-goto [:interact:interact main page]
+goto [[interact|interact main page]]

<<TableOfContents>>
-Line 5:
+Line 7:
-by Marshall Hampton (tested by William Stein, who thinks this is really nice!)
{{{
+by Marshall Hampton 
{{{#!sagecell
-Line 17:
+Line 19:
-    v = [a_list[i].copy() for i in indices]
+    v = [a_list[i][:] for i in indices]
-Line 22:
+Line 24:
-        r[i][i] = (v[i]*v[i])^(1/2)
+        r[i][i] = (v[i]*v[i])**(1/2)
-Line 36:
+Line 38:
-    v = [a_list[i].copy() for i in indices]
+    v = [a_list[i][:] for i in indices]
-Line 45:
+Line 47:
-html('<h2>Numerical instability of the classical Gram-Schmidt algorithm</h2>')
+pretty_print(html('<h2>Numerical instability of the classical Gram-Schmidt algorithm</h2>'))
-Line 50:
+Line 52:
-    html('precision in bits: ' + str(precision) + '<br>')
+    pretty_print(html('precision in bits: ' + str(precision) + '<br>'))
-Line 55:
+Line 57:
-    html('Classical Gram-Schmidt:')
+    pretty_print(html('Classical Gram-Schmidt:'))
-Line 57:
+Line 59:
-    html('Stable Gram-Schmidt:')
+    pretty_print(html('Stable Gram-Schmidt:'))
-Line 60:
+Line 62:
-attachment:GramSchmidt.png
+{{attachment:GramSchmidt.png}}

== Equality of det(A) and det(A.tranpose()) ==
by Marshall Hampton
{{{#!sagecell
srg = srange(-4,4,1/10,include_endpoint=True)
@interact
def dualv(a1=slider(srg,default=1),a2=slider(srg,default=2), a3=slider(srg,default=-1),a4=slider(srg,default=3)):
    A1 = arrow2d([0,0],[a1,a2],rgbcolor='black')
    A2 = arrow2d([0,0],[a3,a4],rgbcolor='black')
    A3 = arrow2d([0,0],[a1,a3],rgbcolor='black')
    A4 = arrow2d([0,0],[a2,a4],rgbcolor='black')
    p1 = polygon([[0,0],[a1,a2],[a1+a3,a2+a4],[a3,a4],[0,0]], alpha=.5)
    p2 = polygon([[0,0],[a1,a3],[a1+a2,a3+a4],[a2,a4],[0,0]],rgbcolor='red', alpha=.5)
    A = matrix([[a1,a2],[a3,a4]])
    pretty_print(html('<h3>The determinant of a matrix is equal to the determinant of the transpose</h3>'))
    pretty_print(html(" $det ($ "%(latex(A),latex(A.transpose()),latex(RR(A.determinant())))))
    show(A1+A2+A3+A4+p1+p2)
}}}
{{attachment:Det_transpose_new.png}}
-Line 68:
+Line 88:
-{{{
@interact
def linear_transformation(theta=slider(0, 2*pi, .1), r=slider(0.1, 2, .1, default=1)):
    A=matrix([[1,-1],[-1,1/2]])
+{{{#!sagecell
@interact
def linear_transformation(A=matrix([[1,-1],[-1,1/2]]),theta=slider(0, 2*pi, .1), r=slider(0.1, 2, .1, default=1)):
-Line 74:
+Line 93:
-    circles = sum([circle((0,0), radius=i, rgbcolor=(0,0,0)) for i in [1..2]])
    print jsmath("v = %s,\; %s v=%s"%(v.n(4),latex(A),w.n(4)))
    show(v.plot(rgbcolor=(1,0,0))+w.plot(rgbcolor=(0,0,1))+circles,aspect_ratio=1)
}}}
attachment:Linear-Transformations.png
+    circles = sum([circle((0,0), radius=i, color='black') for i in [1..2]])
    pretty_print(html(""%tuple(map(latex, [A, v.column().n(4), w.column().n(4)]))))
    show(v.plot(color='red')+w.plot(color='blue')+circles,aspect_ratio=1)
}}}
{{attachment:Linear-Transformations.png}}

== Gerschgorin Circle Theorem ==
by Marshall Hampton.  This animated version requires convert (imagemagick) to be installed, but it can easily be modified to a static version.
The animation illustrates the idea behind the stronger version of Gerschgorin's theorem, which says that if the disks around the eigenvalues are disjoint then there is one eigenvalue per disk.  The proof is by continuity of the eigenvalues under a homotopy to a diagonal matrix. 
{{{#!sagecell
from scipy import linalg
pretty_print(html('<h2>The Gerschgorin circle theorem</h2>'))
@interact
def Gerschgorin(Ain = input_box(default='[[10,1,1/10,0],[-1,9,0,1],[1,0,2,3/10],[-.5,0,-.3,1]]', type = str, label = 'A = '), an_size = slider(1,100,1,1.0)):
    A = sage_eval(Ain)
    size = len(A)
    pretty_print(html(' $A =^{'} + l a t e x (m a t r i x (R e a l F i e l d (10), A)) +^{'}$ '))
    A = matrix(RealField(10),A)
    B = [[0 for i in range(size)] for j in range(size)]
    for i in range(size):
        B[i][i] = A[i][i]
    B = matrix(B)
    frames = []

    centers = [(real(q),imag(q)) for q in [A[i][i] for i in range(size)]]
    radii_row = [sum([abs(A[i][j]) for j in range(i)+range(i+1,size)]) for i in range(size)]
    radii_col = [sum([abs(A[j][i]) for j in range(i)+range(i+1,size)]) for i in range(size)]
    x_min = min([centers[i][0]-radii_row[i] for i in range(size)]+[centers[i][0]-radii_col[i] for i in range(size)])
    x_max = max([centers[i][0]+radii_row[i] for i in range(size)]+[centers[i][0]+radii_col[i] for i in range(size)])
    y_min = min([centers[i][1]-radii_row[i] for i in range(size)]+[centers[i][1]-radii_col[i] for i in range(size)])
    y_max = max([centers[i][1]+radii_row[i] for i in range(size)]+[centers[i][1]+radii_col[i] for i in range(size)])

    if an_size > 1: 
        t_range= srange(0,1+1/an_size,1/an_size)
    else:
        t_range = [1]
    for t in t_range:
        C = t*A + (1-t)*B
        eigs = [CDF(x) for x in linalg.eigvals(C.numpy())]
        eigpoints = points([(real(q),imag(q)) for q in eigs],pointsize = 10, rgbcolor = (0,0,0))
        centers = [(real(q),imag(q)) for q in [A[i][i] for i in range(size)]]
        radii_row = [sum([abs(C[i][j]) for j in range(i)+range(i+1,size)]) for i in range(size)]
        radii_col = [sum([abs(C[j][i]) for j in range(i)+range(i+1,size)]) for i in range(size)]
        scale = max([(x_max-x_min),(y_max-y_min)])
        scale = 7/scale
        row_circles = sum([circle(centers[i],radii_row[i],fill=True, alpha = .3) for i in range(size)])
        col_circles = sum([circle(centers[i],radii_col[i],fill=True, rgbcolor = (1,0,0), alpha = .3) for i in range(size)])
        ft = eigpoints+row_circles+col_circles
        frames.append(ft)
    show(animate(frames,figsize = [(x_max-x_min)*scale,(y_max-y_min)*scale], xmin = x_min, xmax=x_max, ymin = y_min, ymax = y_max))
}}}
{{attachment:Gerschanimate.png}}

{{attachment:Gersch.gif}}
-Line 82:
+Line 150:
-{{{
+{{{#!sagecell
-Line 89:
+Line 157:
-def svd_vis(a11=slider(-1,1,.05,1),a12=slider(-1,1,.05,1),a21=slider(-1,1,.05,0),a22=slider(-1,1,.05,1),ofs= selector(['Off','On'],label='offset image from domain')):
+def svd_vis(a11=slider(-1,1,.05,1),a12=slider(-1,1,.05,1),a21=slider(-1,1,.05,0),a22=slider(-1,1,.05,1),ofs= ('offset image from domain',False)):
-Line 93:
+Line 161:
-    if ofs == 'On':
+    if ofs:
-Line 105:
+Line 173:
-    html('<h3>Singular value decomposition: image of the unit circle and the singular vectors</h3>')
    print jsmath("A = %s  = %s %s %s"%(latex(my_mat), latex(matrix(rf_low,u.tolist())), latex(matrix(rf_low,2,2,[s[0],0,0,s[1]])), latex(matrix(rf_low,vh.tolist())))) 
    image_ell = parametric_plot(rotell(s,u,t, offset),0,2*pi)
+    pretty_print(html('<h3>Singular value decomposition: image of the unit circle and the singular vectors</h3>'))
    pretty_print(html("$A = %s  = %s %s %s$"%(latex(my_mat), latex(matrix(rf_low,u.tolist())), latex(matrix(rf_low,2,2,[s[0],0,0,s[1]])), latex(matrix(rf_low,vh.tolist())))))
    image_ell = parametric_plot(rotell(s,u,t, offset),(0,2*pi))
-Line 110:
+Line 178:
-    show(graph_stuff,frame = False,axes=False,figsize=[fsize,fsize])
}}}
attachment:svd1.png
+    show(graph_stuff,frame = False,axes=False,figsize=[fsize,fsize])}}}
{{attachment:svd1.png}}
-Line 116:
+Line 183:
-{{{
+{{{#!sagecell
-Line 120:
+Line 187:
-    var('x')
-Line 123:
+Line 189:
-    html("<h3>Function plot and its discrete Fourier transform</h3>")
    show(plot(f, pbegin, pend, plot_points = 512), figsize = [4,3])
    f_vals = [f(ind) for ind in srange(pbegin, pend,(pend-pbegin)/512.0)]
+    pretty_print(html("<h3>Function plot and its discrete Fourier transform</h3>"))
    show(plot(f, (x,pbegin, pend), plot_points = 512), figsize = [4,3])
    f_vals = [f(x=ind) for ind in srange(pbegin, pend,(pend-pbegin)/512.0)]
-Line 127:
+Line 193:
-    show(list_plot([abs(x) for x in my_fft], plotjoined=True), figsize = [4,3])
}}}
attachment:dfft1.png
+    show(list_plot([abs(i) for i in my_fft], plotjoined=True), figsize = [4,3])
}}}
{{attachment:dfft1.png}}

== The Gauss-Jordan method for inverting a matrix ==
by Hristo Inouzhe
{{{#!sagecell
#Choose the size D of the square matrix:
D = 3

example = [[1 if k==j else 0 for k in range(D)] for j in range(D)]
example[0][-1] = 2
example[-1][0] = 3

@interact
def _(M=input_grid(D,D, default = example,
                   label='Matrix to invert', to_value=matrix),
      tt = text_control('Enter the bits of precision used'
                        ' (only if you entered floating point numbers)'),  
      precision = slider(5,100,5,20),
      auto_update=False):
    if det(M)==0:
        print 'Failure: Matrix is not invertible'
        return
    if M.base_ring() == RR:
        M = M.apply_map(RealField(precision))
    N=M
    M=M.augment(identity_matrix(D))
    print 'We construct the augmented matrix'
    show(M)
    for m in range(0,D-1):
        if M[m,m] == 0:
            lista = [(abs(M[j,m]),j) for j in range(m+1,D)]
            maxi, c = max(lista)
            M[c,:],M[m,:]=M[m,:],M[c,:]
            print 'We permute rows %d and %d'%(m+1,c+1)
            show(M)
        for n in range(m+1,D):
            a=M[m,m]
            if M[n,m]!=0:
                print "We add %s times row %d to row %d"%(-M[n,m]/a, m+1, n+1)
                M=M.with_added_multiple_of_row(n,m,-M[n,m]/a)
                show(M)
    for m in range(D-1,-1,-1):
        for n in range(m-1,-1,-1):
            a=M[m,m]
            if M[n,m]!=0:
                print "We add %s times row %d to the row %d"%(-M[n,m]/a, m+1, n+1)
                M=M.with_added_multiple_of_row(n,m,-M[n,m]/a)
                show(M)       
    for m in range(0,D):
        if M[m,m]!=1:
            print 'We divide row %d by %s'%(m+1,M[m,m])
            M = M.with_row_set_to_multiple_of_row(m,m,1/M[m,m])
            show(M)   
    M=M.submatrix(0,D,D)
    print 'We keep the right submatrix, which contains the inverse'        
    html(' $M^{- 1} =$ '%latex(M))
    print 'We check it actually is the inverse'
    html(' $M^{- 1} * M =$ '%(latex(M),latex(N),latex(M*N)))
}}}
{{attachment:gauss-jordan.png}}

...(goes all the way to invert the matrix)

== Solution of an homogeneous system of linear equations ==
by Pablo Angulo 

Coefficients are introduced as a matrix in a single text box.
The number of equations and unknowns are arbitrary.

{{{#!sagecell
from sage.misc.html import HtmlFragment

def HSLE_as_latex(A, variables):
    nvars = A.ncols()
    pretty_print(HtmlFragment( 
    r' $Math input error$ '))

@interact
def SEL(A='[(0,1,-1,2),(-1,0,2,4), (0,-1,1,-2)]',
        auto_update=False
    ):
    M = A = matrix(eval(A))
    neqs = M.nrows()
    nvars = M.ncols()
    var_names = ','.join('x%d'%j for j in [1..nvars])
    variables = var(var_names)

    HSLE_as_latex(M, variables)
    pretty_print(HtmlFragment( 'SEL in matrix form'))
    show(M)

    pivot = {}
    ibound_variables = []
    for m,row in enumerate(M):
        for k in range(m,nvars):
            lista = [(abs(M[j,k]),j) for j in range(m,neqs)]
            maxi, c = max(lista)
            if maxi > 0:
                ibound_variables.append(k)
                if M[m,k]==0:
                    M[c,:],M[m,:]=M[m,:],M[c,:]
                    pretty_print( HtmlFragment('We permute rows %d and %d'%(m+1,c+1)))
                    show(M)
                pivot[m] = k
                break

        a=M[m,k]
        for n in range(m+1,neqs):
            if M[n,k]!=0:
                pretty_print( HtmlFragment("We add %s times row %d to row %d"%(-M[n,k]/a, m+1, n+1)))
                M=M.with_added_multiple_of_row(n,m,-M[n,k]/a)
                show(M)

    pretty_print( HtmlFragment('Equivalent system of equations'))
    HSLE_as_latex(M, variables)
    SEL_type = 'compatible'
    null_rows = None
    for k,row in enumerate(M):
        if row.is_zero():
            pretty_print( HtmlFragment('We remove trivial 0=0 equations'))
            M = M[:k,:]
            HSLE_as_latex(M, variables)
                
    ifree_variables = [k for k in range(nvars) if k not in ibound_variables]
    bound_variables = [variables[k] for k in ibound_variables]
    free_variables = [variables[k] for k in ifree_variables]
    pretty_print( HtmlFragment('Free variables: %s'% free_variables))
    pretty_print( HtmlFragment('Bound variables: %s'% bound_variables))
    reduced_eqs = [
        sum(c*v for c,v in zip(row, variables))==0
        for row in M
    ]
    xvector = vector(variables)
    if len(bound_variables)==1:
        soldict = solve(reduced_eqs, bound_variables[0], solution_dict=True)[0]
    else:
        soldict = solve(reduced_eqs, bound_variables, solution_dict=True)[0]

    pretty_print( HtmlFragment('Solution in parametric form'))
    parametric_sol = matrix(
        xvector.apply_map(lambda s:s.subs(soldict))
    ).transpose()
    show(parametric_sol)
    pretty_print( HtmlFragment('Generators'))
    pretty_print( HtmlFragment(
        r' $⟨$ '%','.join(latex(
            parametric_sol.subs(dict((variables[k],1 if j==k else 0) for k in ifree_variables))
        ) for j in ifree_variables)
    ))
    pretty_print( HtmlFragment('Dimension is %d'%len(free_variables)))
}}}
{{attachment:HSEL_1.png}}
{{attachment:HSEL_2.png}}

Diff for "interact/linear_algebra"

Sage Interactions - Linear Algebra

Numerical instability of the classical Gram-Schmidt algorithm

Equality of det(A) and det(A.tranpose())

Linear transformations

Gerschgorin Circle Theorem

Singular value decomposition

Discrete Fourier Transform

The Gauss-Jordan method for inverting a matrix

Solution of an homogeneous system of linear equations