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Classroom Figures forthe Conjugate Gradient Method
Without the Agonizing PainEdition 1 1
4
Jonathan Richard Shewchuk
August 4, 1994
School of Computer ScienceCarnegie Mellon University
Pittsburgh, PA 15213
Abstract
This report contains a set of full-page figures designed to be used as classroom transparencies for teaching from thearticle “An Introduction to the Conjugate Gradient Method Without the Agonizing Pain”.
Supported in part by the Natural Sciences and Engineering Research Council of Canada under a 1967 Science and EngineeringScholarship and by the National Science Foundation under Grant ASC-9318163. The views and conclusions contained in thisdocument are those of the author and should not be interpreted as representing the official policies, either express or implied, ofNSERC, NSF, or the U.S. Government.
Keywords: conjugate gradient method, transparencies, agonizing pain
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
3 �1�
2 �2 � 2
2 �1�
6 �2 � � 8
Sample 2-d linear system of the form � � � � :���� 3 22 6
�� � ����� 2� 8
���
-4
-20
24
6
-6
-4
-2
0
2
4
0
50
100
150
-4
-20
24
6
�1
�2
��� ���
�1
Graph of quadratic form��� ��� � 1
2��� � � � � � � � �
. Theminimum point of this surface is the solution to � � � � .
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
Contours of the quadratic form. Each ellipsoidal curve hasconstant
��� �� .
-4 -2 2 4 6
-8
-6
-4
-2
2
4
6
�1
�2
Gradient� � � ��� of the quadratic form. For every � , the
gradient points in the direction of steepest increase of��� ��� ,
and is orthogonal to the contour lines.
(c)
�1
�2
��� ��
�1
(d)
�1
�2
��� ���
�1
(a)
�1
�2
��� ��
�1
(b)
�1
�2
��� ���
�1
(a) Quadratic form for a positive-definite matrix.
(b) For a negative-definite matrix.
(c) For a singular (and positive-indefinite) matrix. A linethat runs through the bottom of the valley is the set ofsolutions.
(d) For an indefinite matrix.
0.2 0.4 0.6
20406080
100120140
-4 -2 2 4 6
-6
-4
-2
2
4
-4 -2 2 4 6
-6
-4
-2
2
4
-2.50
2.55
-5-2.50
2.5050100150
-2.50
2.55
�
(c)��� ������� � ������� �
�1
(d)�
2
���1�
�1
(a)�
2
���0� � �
0� �
(b)
�1
�2
��� ���
�1
The method of Steepest Descent.
-2 -1 1 2 3
-3
-2
-1
1
2
�1
�2
Solid arrows: Gradients.
Dotted arrows: Slope along search line.
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
���0� �
The method of Steepest Descent.
B v
B vv
2
3Bv
� is an eigenvector of�
with a corresponding eigenvalueof � 0 5. As � increases,
� � � converges to zero.
Bvv B v B v2 3
Here, � has a corresponding eigenvalue of 2. As � increases,� � � diverges to infinity.
B x
B x2
3
x
Bxv v1 2
� � �1� �
2. One eigenvector diverges, so � also diverges.
-4 -2 2 4 6
-6
-4
-2
2
4
72
�1
�2
The eigenvectors of � are directed along the axes of theparaboloid defined by the quadratic form
��� �� . Each eigen-vector is labeled with its associated eigenvalue.
-4 -2 2 4 6
-6
-4
-2
2
4
�1
(a)�
2
� 0 470 47
-4 -2 2 4 6
-6
-4
-2
2
4
�1
(b)�
2
���0� �
-4 -2 2 4 6
-6
-4
2
4
�1
(c)�
2
� �0�
-4 -2 2 4 6
-6
-4
-2
2
4
�1
(d)�
2
� �1�
-4 -2 2 4 6
-6
-4
-2
2
4
�1
(e)�
2
� �2�
-4 -2 2 4 6
-6
-4
-2
2
4
�1
(f)�
2
Convergence of the Jacobi Method.
In (a), the eigenvectors of�
are shown with their corre-sponding eigenvalues. These eigenvectors are NOT theaxes of the paraboloid.
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
Steepest Descent converges to the exact solution on the firstiteration if the error term is an eigenvector.
-4 -2 2 4 6
-4
-2
2
4
6
�1
�2
Steepest Descent converges to the exact solution on the firstiteration if the eigenvalues are all equal.
-6 -4 -2 2
-2
2
4
6
8
�1
�2
The energy norm of these two vectors is equal.
0
5
10
15
20
1
20
40
60
80
100
0
0.2
0.4
0.6
0.8
0
5
10
15
20
�
�
�
�
Convergence � of Steepest Descent.� is the slope of
� ����� with respect to the eigenvector axes.� is the condition number of � .
Convergence is worst when � � � � .
-4 -2 2 4
-4
-2
2
4
6
-4 -2 2 4
-4
-2
2
4
6
-4 -2 2 4
-4
-2
2
4
6
-4 -2 2 4
-4
-2
2
4
6
�1
(c)�
2
�1
(d)�
2
�1
(a)�
2
�1
(b)�
2
(a) Large � , small � .
(b) An example of poor convergence. � and � are bothlarge.
(c) Small � and � .
(d) Small � , large � .
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
���0�
Solid lines: Worst starting points for Steepest Descent.
Dashed lines: Steps toward convergence.
Grey arrows: Eigenvector axes.
Here, � � 3 5.
20 40 60 80 1000
0.2
0.4
0.6
0.8
1
�
�
Convergence of Steepest Descent (per iteration) worsens asthe condition number of the matrix increases.
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
���0�
����
1�
� �1�
� �0�
The Method of Orthogonal Directions.
-4 -2 2 4
-4
-2
2
4
�1
�2
These pairs of vectors are � -orthogonal
-4 -2 2 4
-4
-2
2
4
�1
�2
because these pairs of vectors are orthogonal.
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
� �0�
�� �
1� � �
1�
� �0�
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
� �0�
The method of Conjugate Directions converges in � steps.� �
1� must be � -orthogonal to
� �0� .
d
du
u
u
u
+
*
d0
1
(0)(0)
(1)
Gram-Schmidt conjugation of two vectors.
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
The method of Conjugate Directions using the axial unitvectors, also known as Gaußian elimination.
dd(0)
(1)
e (2)e(0) (1)
e
0
The shaded area is� �
0� � � � � �
0� � span � � �
0��� � �
1��� .
The ellipsoid is a contour on which the energy norm isconstant.
After two steps, CG finds� �
2� , the point on
� �0� � �
thatminimizes � � ��� .
� �1�
�
� �1�
� �0�
� �1�
� �1�
���0�
���0� � �
1
(a)
�� �
1����
1�
� �0�
� �0�
� �1�
� �1�
���0� � �
1
(b)
����
1�
���2�
� �0�
���0� � �
2
� �0�
��1�
� �1�
� �0� � �
1
(c)
���� 2 �� �1�
���1�
� �0�
���0�
� �1� ���
0� � �
2
���0� � �
1
(d)
(a) 2D problem.
(b) Stretched 2D problem.
(c) 3D problem.
(d) Stretched 3D problem.
dd
d
r
(0)
(1)
(2) (2)
uu10
u2
e(2)
� �0� and
� �1� span the same subspace as �
0� �
1 (the gray-colored plane
�2).
� �2� is � -orthogonal to
�2.
� �2� is orthogonal to
�2.
� �2� is constructed (from �
2) to be � -orthogonal to�
2.
dd
dr
(0)(1)
(2)(2)
rr(0)(1)
e(2)
� �0� and
� �1� span the same subspace as � �
0� � � �
1� (the gray-
colored plane�
2).� �
2� is � -orthogonal to
�2.
� �2� is orthogonal to
�2.
� �2� is constructed (from � �
2� ) to be � -orthogonal to
�2.
-4 -2 2 4 6
-6
-4
-2
2
4
�1
�2
���0� �
The method of Conjugate Gradients.
2 7
-1-0.75-0.5
-0.25
0.250.5
0.751
2 7
-1-0.75-0.5
-0.25
0.250.5
0.751
2 7
-1-0.75-0.5
-0.25
0.250.5
0.751
2 7
-1-0.75-0.5
-0.25
0.250.5
0.751
�
(c)�
2� � �
�
(d)�
2� � �
�
(a)�
0� � �
�
(b)�
1� � �
The convergence of CG after � iterations depends on howclose a polynomial
� � of degree � can be to zero on eacheigenvalue, given the constraint that
� � � 0 � � 1.
-1 -0.5 0.5 1
-2-1.5
-1-0.5
0.51
1.52
-1 -0.5 0.5 1
-2-1.5
-1-0.5
0.51
1.52
-1 -0.5 0.5 1
-2-1.5
-1-0.5
0.51
1.52
-1 -0.5 0.5 1
-2-1.5
-1-0.5
0.51
1.52
�
�10� � �
�
�49� � �
�
�2� � �
�
�5� � �
Chebyshev polynomials of degree 2, 5, 10, and 49.
1 2 3 4 5 6 7 8
-1
-0.75
-0.5
-0.25
0.25
0.5
0.75
1
�
�2� � �
The optimal polynomial�
2� � � for
����� � 2 and
������ � 7
in the general case.
� � � � is reduced by a factor of at least 0.183 after twoiterations of CG.
20 40 60 80 1000
0.2
0.4
0.6
0.8
1
�
�
Convergence of Conjugate Gradients (per iteration) as afunction of condition number.
200 400 600 800 10000
5
10
15
20
25
30
35
40
�
�
Number of iterations of Steepest Descent required to matchone iteration of CG.
-4 -2 2 4 6
-8
-6
-4
-2
�1
�2
Contour lines of the quadratic form of the diagonally pre-conditioned sample problem. The condition number hasimproved from 3 5 to roughly 2 8.
-4
-20
24
6
-2
0
2
4
6
-250
0
250
500
-4
-20
24
6
(a)
�1
�2
��� ���
1
-4 -2 2 4 6
-2
2
4
6
�1
(b)�
2
���0�
-0.04 -0.02 0.02 0.04
-200
200
400
600
�
(c)��� ������� � � � ����� �
-4 -2 2 4 6
-2
2
4
6
�1
(d)�2
���0�
The nonlinear Conjugate Gradient Method.
(b) Fletcher-Reeves CG.
(c) Cross-section of the surface corresponding to the firststep of Fletcher-Reeves.
(d) Polak-Ribiere CG.
-4 -2 2 4 6
-2
2
4
6
�1
�2
� �0�
Nonlinear CG can be more effective with periodic restarts.
-1 -0.5 0.5 1 1.5 2
-1
-0.75
-0.5
-0.25
0.25
0.5
0.75
1
�
�
�
The Newton-Raphson method.
Solid curve: The function to minimize.
Dashed curve: Parabolic approximation to the function,based on first and second derivatives at � .
� is chosen at the base of the parabola.
-1 1 2 3 4
-1.5
-1
-0.5
0.5
1
�
�
�
The Secant method.
Solid curve: The function to minimize.
Dashed curve: Parabolic approximation to the function,based on first derivatives at � � 0 and � � 2.
� is chosen at the base of the parabola.
-4 -2 2 4 6
-2
2
4
6
�1
�2
���0�
The preconditioned nonlinear Conjugate Gradient Method.
Polak-Ribiere formula and a diagonal preconditioner.
The space has been “stretched” to show the improvementin circularity of the contour lines around the minimum.
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