Applied Mathematics
Vol.05 No.17(2014), Article ID:50345,6 pages
10.4236/am.2014.517252
A New Scheme for Discrete HJB Equations
Zhanyong Zou
School of Mathematics and Statistics, Guangdong University of Finance & Economics, Guangzhou, China
Email: yong_china@126.com
Copyright © 2014 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/



Received 2 August 2014; revised 28 August 2014; accepted 10 September 2014
ABSTRACT
In this paper we propose a relaxation scheme for solving discrete HJB equations based on scheme II [1] of Lions and Mercier. The convergence of the new scheme has been established. Numerical example shows that the scheme is efficient.
Keywords:
Iterative Algorithm, Relaxation Scheme, HJB Equation, Convergence, Existence

1. Introduction
Consider the following Hamilton-Jacobi-Bellman (HJB) equation:
(1.1)
where
is a bounded domain in
are elliptic operators of second order. Equation (1.1) is arising in stochastic control problems. See [2] and the references therein.
Equation (1.1) can be discretized by finite difference method or finite element method. See [1] [3] and the references therein. Then we obtain the following discrete HJB equation:
(1.2)
where
. Equation (1.2) is a system of nonsmooth nonlinear equations. Many numerical algorithms for solving (1.2) have been proposed. See [4] -[12] and the references therein.
[1] has given two iterative algorithms for solving (1.2). At each iteration, a linear complementarity subproblem or a linear equation system subproblem is solved. See also [4] .
Scheme I.
Step 1: Given
for some
we find
such that

Step 2: Let
For
we find
such that

Step 3: If
then the output is
otherwise
and it goes to Step 2.
Assume 

That is: the lth row of matrix 



Scheme II.
Step 1: 



Step 2: For 


Step 3: Compute 

Step 4: If 


In the last decade many numerical schemes have been given for solving (1.2). But the above schemes are still playing a very important role. See [4] -[6] and the references therein.
In this paper we propose, based on Scheme II above, a relaxation scheme with a parameter



2. New Scheme and Convergence
We propose a new scheme which is an extension of Scheme II.
New Scheme II.
Step 1: Given 




Step 2: For 


Step 3: Compute 

Step 4: Compute

Step 5: If 


In [13] we proposed the following conditions for (1.2).
Condition 

In [13] we have proved the following theorem.
Theorem 2.1 If Condition 
We have the following convergence theorem.
Theorem 2.2 Assume that Condition 


Proof Since all 

First, we prove 

By (2.3) we have

which combining with (2.1) and (2.2) yields

Since 

By (2.4) we obtain

By

and

which and (2.10) implies
Similarly, by (2.3) we derive
which combining with (2.2) and (2.6) implies
Hence we have

By (2.4), we have

By (2.12), (2.13) and

which combining with 

By (2.11), (2.12) and (2.13) ,we get
which combining with (2.15) implies
It is easy to derive by induction that

and

It follows that (2.5) holds.
It follows from (2.2) and (2.3) that

Since the set 



Therefore, we have
Then by (2.2) we obtain
which and (2.17) results in

From (2.4), (2.16) and (2.19) we have

It follows from (2.18), (2.19) and (2.20) that
which means 
Finally, we prove the uniqueness of solution. Assume 



It is easy to see from (2.21) and (2.22) that there exist 





(2.23) and (2.26) implie


3. Numerical Example
We use example 2 in [4] , i.e.,

where
The discretization of the above second order derivatives are:
where 






Table 1 and Table 2 show the ∞-norm of the residual 
We see that 



Table 3 shows the relation between iteration number 





We can see from Table 3 that the algorithm for 

Table 1. ∞-norm of the residual R.
Table 2. ∞-norm of the residual R.
Table 3. Iteration number m.
Table 4. The value of 

Table 5. The value of 

Funding
This work was supported by Educational Commission of Guangdong Province, China (No. 2012LYM-0066) and the National Social Science Foundation of China (No. 14CJL016).
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