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Solutions to problems related to the convergence of fixed point iteration for solving fixed point problems and the use of implicit methods for solving differential equations. The conditions for convergence, the restrictions on timesteps for backward euler and trapezoidal methods, and the utility of implicit methods for stiff systems.
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Jeffrey Hellrung
Friday, November 23, 2005
Math 269A, Assignment 08
G(xn) for n = 0, 1 , 2 ,.. ..
(a) Give conditions on G and the initial iterate x 0 that will insure that the fixed point iteration
converges.
(b) Assume one is using Backward Euler to solve the differential equation dy/dt = f (y). If the implicit
equation that arises is solved using fixed point iteration, what is the restriction on the timestep
that will ensure that the iteration converges?
(c) Assume one is using the trapezoidal method to solve the differential equation dy/dt = f (y). If
the implicit equation that arises is solved using fixed point iteration, what is the restriction on
the timestep that will ensure that the iteration converges?
(d) What conclusion can you draw about the utility of using implicit ODE methods for stiff systems
when the implicit equations that arise are solved using fixed point iteration?
Solution
(a) Let x ∗ be a fixed point of G (supposing one exists), i.e., G(x ∗ ) = x ∗
. Then fixed point iteration
converges if G is a contraction mapping within a neighborhood of x
∗ and the initial iterate x 0 lies
in this neighborhood. Explicitly, we want
|xn+1 − x
∗ | ≤ α|xn − x
∗ |
for some 0 < α < 1. In this case, we have
G(xn) − G(x ∗ )
xn − x ∗
≤ α < 1 ,
and by the Mean Value Theorem, the difference quotient is the value of |G ′ | somewhere between
xn and x
∗
. It follows that if G
′ is bounded in absolute value by some α < 1 in some neighborhood
of x
∗ , then if the initial iterate x 0 lies in this neighborhood, fixed point iteration will converge,
i.e., xn → x
∗ .
Indeed, if |G
′ | < α uniformly, then there exists a unique fixed point, and fixed point iteration will
converge for any initial iterate.
(b) The Backward Euler method takes the form
yk+1 = yk + hf (yk+1),
hence we wish to solve
x = G(x) = yk + hf (x).
By the comments above, this will have a unique fixed point, and fixed point iteration will converge
for any initial iterate, if
′ (x)| = h
∂f
∂y
(x)
≤ α < 1
for all x ∈ R.
(c) The Trapezoidal method takes the form
yk+1 = yk +
h
f (yk) +
h
f (yk+1),
hence we wish to solve
x = G(x) = yk +
h
f (yk) +
h
f (x).
Thus we require that
′ (x)| =
h
∂f
∂y
(x)
(d) Using implicit methods for solving stiff ODEs by fixed point iteration provides little advantage,
if any, over explicit methods, since similar time step restrictions are necessary.
du
dt
(1 + (u + 1000)(u + 1)) ,
dv
dt
1 + v
2
(a) Give an expression for the Jacobian of this system.
(b) What are the eigenvalues of the Jacobian when u = 0.0 and v = 0.5?
(c) Assuming that u is close to 0.0 and v is close to 0.5, estimate the timestep required to obtain a
qualitatively correct solution when a fourth order Runge-Kutta method is used.
(d) Assume one is using Backward Euler to solve this system. Write out, in component form, the
iteration that one must implement if one is going to use Newton’s method to solve the non-linear
equations that must be solved to advance the solution.
Solution
(a) We have
F (u, v) =
1 100
1 100
(1 + (u + 1000)(u + 1)) 1 100
1 100
1 + v
2
so
JF(u,v) =
− 3 u 2 − 2 uv − 100101 u/ 50 − 1001 v − 101101 / 100 −u 2 − 1001 u − 1001
−v 2 − 1 − 3 v 2 − 2 uv − v/ 50 − 1
(b) At (u, v) = (0. 0 , 0 .5), the Jacobian is
which has eigenvalues
λ 1 = − 1512. 34 , λ 2 = − 0. 931675.
(c) We require both hλ 1 and hλ 2 to be in the region of absolute stability, hence h ∈ (0, 0 .00184171).