I. Basic math.
 II. Pricing and Hedging.
 III. Explicit techniques.
 IV. Data Analysis.
 V. Implementation tools.
 VI. Basic Math II.
 VII. Implementation tools II.
 1 Calculational Linear Algebra.
 2 Wavelet Analysis.
 3 Finite element method.
 4 Construction of approximation spaces.
 5 Time discretization.
 6 Variational inequalities.
 A. Stationary variational inequalities.
 B. Evolutionary variational inequalities.
 VIII. Bibliography
 Notation. Index. Contents.

## Variational inequalities.

n this chapter we consider a technique that extends the finite elements (discussed in the chapter ( Finite elements )) to financial instruments involving stochastic optimal control (American feature). The reference is [Bensoussan] .

We previously introduced a backward induction and Bellman equation in the sections ( Backward induction ) and ( Bellman equation ). We now study variational inequalities because direct application of Bellman equation is not possible in combination with Finite Element technique. Indeed, when conducting Finite Element calculations, we evolve a vector of coordinates with respect to some finite element basis. Bellman equation requires us to continuously take maximum among some function values. We would like to avoid assembling and disassembling basis decompositions on every time step. In multidimensional situation such operation is not even feasible.

To see how a stochastic control problem may lead to a variational inequality consider first the following problem of evaluation of : where the is the standard Brownian motion, the is the first exit time of the process from for , is a bounded set with smooth boundary and is a function and , . We saw in the section ( Representation of solution for elliptic PDE using stochastic process ) that the solves the following problem We apply such result in context of the section ( Optimal stopping time problem ). We modify the task to find the function defined by the relationships Combining the calculations of the sections ( Representation of solution for elliptic PDE using stochastic process ) and ( Optimal stopping time problem ) we conclude that such solves the following free boundary problem almost everywhere in and

Observe that the above problem may be rewritten as

 (Variational inequality example)
where the bilinear form (compare with the section ( Elliptic PDE section )) is given by and the class of functions is defined as

To see the equivalence of the two formulations consider the area where . We can find two functions and from such that and . Then for implies . One the other hand, in the area where we always have is nonpositive for and thus implies .

 A. Stationary variational inequalities.
 B. Evolutionary variational inequalities.
 Notation. Index. Contents.