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EE 616 Computer Aided Analysis of Electronic Networks
Lecture 5
Instructor: Dr. J. A. Starzyk, ProfessorSchool of EECSOhio UniversityAthens, OH, 45701
09/19/2005
Note: materials in this lecture are from the notes of EE219A UC-berkeleyhttp://www- cad.eecs.berkeley.edu/~nardi/EE219A/contents.html
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Outline
Nonlinear problems Iterative Methods Newton’s Method
– Derivation of Newton– Quadratic Convergence– Examples– Convergence Testing
Multidimensonal Newton Method– Basic Algorithm – Quadratic convergence– Application to circuits
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Need to Solve
( 1) 0d
t
VV
d sI I e
0
1
IrI1 I
d
0)1(1
0
11
1
1
IeIeR
III
tV
e
s
dr
11)( Ieg
DC Analysis of Nonlinear Circuits - Example
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Nonlinear Equations
Given g(V)=I It can be expressed as: f(V)=g(V)-I
Solve g(V)=I equivalent to solve f(V)=0
Hard to find analytical solution for f(x)=0
Solve iteratively
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Nonlinear Equations – Iterative Methods
Start from an initial value x0
Generate a sequence of iterate xn-1, xn, xn+1 which hopefully converges to the solution x*
Iterates are generated according to an iteration function F: xn+1=F(xn)
Ask• When does it converge to correct solution ?• What is the convergence rate ?
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Newton-Raphson (NR) Method
Consists of linearizing the system.Want to solve f(x)=0 Replace f(x) with its linearized version and solve.
Note: at each step need to evaluate f and f’
functionIterationxfxdx
dfxx
xxxdx
dfxfxf
iesTaylor Serxxxdx
dfxfxf
kkkk
kkkkk
)()(
))(()()(
))(()()(
11
11
***
7
Newton-Raphson Method – Graphical View
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Newton-Raphson Method – Algorithm
Define iteration
Do k = 0 to ….?
until convergence
How about convergence? An iteration {x(k)} is said to converge with order q if there exists a
vector norm such that for each k N:
qkk xxxx ˆˆ1
)()(1
1 kkkk xfxdx
dfxx
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2* * * 2
2
*
0 ( ) ( ) ( )( ) ( )( )
some [ , ]
k k k k
k
df d ff x f x x x x x x x
dx dx
x x x
Mean Value theoremtruncates Taylor series
But
10 ( ) ( )( )k k k kdff x x x x
dx by Newton
definition
Newton-Raphson Method – Convergence
10
Subtracting
21 * 1 * 2
2 ( ) [ ( )] ( )( )k k kdf d f
x x x x x xdx d x
Convergence is quadratic
21 * * 2
2( )( ) ( )( )k k kdf d fx x x x x x
dx d x
Dividing
21
2
21 * *
Let [ ( )] ( )
then
k k
k k k
df d fx x K
dx d x
x x K x x
Newton-Raphson Method – Convergence
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Local Convergence Theorem
If
Then Newton’s method converges given a sufficiently close initial guess (and convergence is quadratic)
boundedisK
boundeddx
fdb
roay from zebounded awdx
dfa
)
)
2
2
Newton-Raphson Method – Convergence
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0 Initial Guess,= 0x k
Repeat { 1
k
k k kf x
x x f xx
} Until ?
1 1? ?k k kf x thresholdx x threshold
1k k
Newton-Raphson Method – Convergence
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Need a "delta-x" check to avoid false convergence
X
f(x)
1kx kx *x
1 a
kff x
1 1 a r
k k kx xx x x
Newton-Raphson Method – Convergence Check
False convergence since increment in f small but with large increment in X
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Also need an " " check to avoid false convergencef x
X
f(x)
1kx kx
*x
1 a
kff x
1 1 a r
k k kx xx x x
Newton-Raphson Method – Convergence Check
False convergence since increment in x small but with large increment in f
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Newton-Raphson Method – Convergence
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Convergence Depends on a Good Initial Guess
X
f(x)
0x
1x2x 0x
1x
Newton-Raphson Method – Local Convergence
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Convergence Depends on a Good Initial Guess
Newton-Raphson Method – Local Convergence
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Nodal Analysis
1 2At Node 1: 0i i
NonlinearResistors
i g v
1bv
+
-
1i2i 2
bv
3i+ -
3bv
+
-
1v2v
1 1 2 0g v g v v
3 2At Node 2: 0i i
3 1 2 0g v g v v
Two coupled nonlinear equations in two unknowns
Nonlinear Problems – Multidimensional Example
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* *Problem: Find such tha 0t x F x
Multidimensional Newton Method
functionIterationxFxJxx
atrixJacobian M
x
xF
x
xF
x
xF
x
xF
xJ
iesTaylor SerxxxJxFxF
kkkk
N
NN
N
)()(
)()(
)()(
)(
))(()()(
11
1
1
1
1
***
20
)()()( :solve Instead
sparse).not is(it )( computenot Do
)()(:
1
1
11
kkkk
k
kkkk
xFxxxJ
xJ
xFxJxxIteration
Each iteration requires:
1. Evaluation of F(xk)
2. Computation of J(xk)
3. Solution of a linear system of algebraic equations whose coefficient matrix is J(xk) and whose RHS is -F(xk)
Multidimensional Newton Method – Computational Aspects
21
0 Initial Guess,= 0x k
Repeat {
1 1Solve for k k k k kFJ x x x F x x
} Until 11 , small en ug o hkk kx x f x
1k k
Compute ,k kFF x J x
Multidimensional Newton Method – Algorithm
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If
1) Inverse is boundedkFa J x
) Derivative is Lipschitz ContF Fb J x J y x y
Then Newton’s method converges given a sufficiently close initial guess (and convergence is quadratic)
Local Convergence Theorem
Multidimensional Newton Method – Convergence
23
Application of NR to Circuit EquationsCompanion Network
Applying NR to the system of equations we find that at iteration k+1:– all the coefficients of KCL, KVL and of BCE of the linear
elements remain unchanged with respect to iteration k– Nonlinear elements are represented by a linearization of
BCE around iteration k
This system of equations can be interpreted as the STA of a linear circuit (companion network) whose elements are specified by the linearized BCE.
24
General procedure: the NR method applied to a nonlinear circuit (whose eqns are formulated in the STA form) produces at each iteration the STA eqns of a linear resistive circuit obtained by linearizing the BCE of the nonlinear elements and leaving all the other BCE unmodified
After the linear circuit is produced, there is no need to stick to STA, but other methods (such as MNA) may be used to assemble the circuit eqns
Application of NR to Circuit EquationsCompanion Network
25
Note: G0 and Id depend on the iteration count k G0=G0(k) and Id=Id(k)
Application of NR to Circuit EquationsCompanion Network – MNA templates
26
Application of NR to Circuit EquationsCompanion Network – MNA templates
27
Modeling a MOSFET(MOS Level 1, linear regime)
d
28
Modeling a MOSFET(MOS Level 1, linear regime)
gds
29
DC Analysis Flow Diagram
For each state variable in the system
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Implications
Device model equations must be continuous with continuous derivatives and derivative calculation must be accurate derivative of function
Watch out for floating nodes (If a node becomes disconnected, then J(x) is singular)
Give good initial guess for x(0)
Most model computations produce errors in function values and derivatives.
– Want to have convergence criteria || x(k+1) - x(k) || < such that > than model errors.
31
Summary
Nonlinear problems Iterative Methods Newton’s Method
– Derivation of Newton– Quadratic Convergence– Examples– Convergence Testing
Multidimensional Newton Method– Basic Algorithm – Quadratic convergence– Application to circuits
32
Improving convergence
Improve Models (80% of problems) Improve Algorithms (20% of problems)
Focus on new algorithms:Limiting Schemes
Continuations Schemes
33
Outline
Limiting Schemes– Direction Corrupting– Non corrupting (Damped Newton)
Globally Convergent if Jacobian is NonsingularDifficulty with Singular Jacobians
Continuation Schemes– Source stepping– More General Continuation Scheme– Improving Efficiency
Better first guess for each continuation step
34
At a local minimum, 0f
x
LocalMinimum
Multidimensional Case: is singularFJ x
Multidimensional Newton MethodConvergence Problems – Local Minimum
35
f(x)
X0x
1x
Must Somehow Limit the changes in X
Multidimensional Newton MethodConvergence Problems – Nearly singular
36
Multidimensional Newton MethodConvergence Problems - Overflow
f(x)
X0x 1x
Must Somehow Limit the changes in f(x)
37
0 Initial Guess,= 0x k Repeat {
1 1Solve for k k k kFJ x x F x x
} Until 1 1 , small enoughkkx F x 1k k
Compute ,k kFF x J x
Newton Algorithm for Solving 0F x
1 1limitedk k kx x x
Newton Method with Limiting
38
1limited k
ix
• NonCorrupting
1 1 if k ki ix x
1 otherwisekisign x
• Direction Corrupting
1 1limited k kx x
1min 1,
kx
1kx 1limited kx
1kx 1limited kx
Heuristics, No Guarantee of Global Convergence
Newton Method with LimitingLimiting Methods
Scales proportionally In all dimensions
Scales differently different dimensions
39
General Damping Scheme
Key Idea: Line Search
21
2Pick to minimize k k k kF x x
Method Performs a one-dimensional search in Newton Direction
21 1 1
2
Tk k k k k k k k kF x x F x x F x x
1 1k k k kx x x
1 1Solve for k k k kFJ x x F x x
Newton Method with LimitingDamped Newton Scheme
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If
1) Inverse is boundedkFa J x
) Derivative is Lipschitz ContF Fb J x J y x y
Then
There exists a set of ' 0,1 such thatk s
1 1 with <1k k k k kF x F x x F x
Every Step reduces F-- Global Convergence!
Newton Method with LimitingDamped Newton – Convergence Theorem
41
X0x1x
2x1Dx
Damped Newton Methods “push” iterates to local minimumsFinds the points where Jacobian is Singular
Newton Method with LimitingDamped Newton – Singular Jacobian Problem
42
Solve , 0 where:F x
a) 0 ,0 0 is easy to solveF x
b) 1 ,1F x F x
c) is sufficiently smoothx
Starts the continuation
Ends the continuation
Hard to insure!
Newton with Continuation schemes Basic Concepts - General setting
Newton converges given a close initial guess
Idea: Generate a sequence of problems, such that a problem is a good initial guess for the following one
0)(xF
43
Solve 0 ,0 , 0prevF x x x
While 1 {
}
0prevx x
Try to Solve , 0 with NewtonF x
If Newton Converged
, 2,prevx x Else
,1
2 prev
Newton with Continuation schemes Basic Concepts – Template Algorithm
44
Newton with Continuation schemes Basic Concepts – Source Stepping Example
45
Diode
1, 0diode sf v i v v V
R
+-Vs
R v
, 1Not dependent!diodef v i v
v v R
Source Stepping Does Not Alter Jacobian
Newton with Continuation schemes Basic Concepts – Source Stepping Example
46
0 ,0= 0 00 F x x Observations
0 ,0F xI
x
=1 1 ,1 1F x F x
0 ,0 1F x F x
x x
Problem is easy to solve and Jacobian definitely
nonsingular.
Back to the original problem and original Jacobian
, 1F x F x x
Newton with Continuation schemes Jacobian Altering Scheme
(1),1)
47
Summary
Newton’s Method works fine: – given a close enough initial guess
In case Newton does not converge:– Limiting Schemes
Direction CorruptingNon corrupting (Damped Newton)
– Globally Convergent if Jacobian is Nonsingular– Difficulty with Singular Jacobians
– Continuation SchemesSource steppingMore General Continuation Scheme Improving Efficiency
– Better first guess for each continuation step