## Section: Scientific Foundations

### Hamilton-Jacobi-Bellman approach

This approach consists in calculating the value function associated with the optimal control problem, and then synthesizing the feedback control and the optimal trajectory using Pontryagin's principle. The method has the great particular advantage of reaching directly the global optimum, which can be very interesting, when the problem is not convex.

*Characterization of the value function*
From the dynamic programming principle, we derive a characterization of the value function
as being a solution (in viscosity sense) of an Hamilton-Jacobi-Bellman equation, wich is a nonlinear PDE of dimension equal to the number n of state variables. Since the pioneer works of Crandall and Lions [51] , [52] , [50] , many theoretical contributions were carried out, allowing an
understanding of the properties
of the value function as well as of the set of admissible trajectories.
However, there remains an important effort to provide for the
development of effective and adapted numerical tools, mainly because of numerical complexity (complexity is exponential with respect to n).

*Numerical approximation for continuous value function*
Several numerical schemes have been already studied to treat the case when the solution of the HJB equation (the value function) is continuous. Let us quote for example the Semi-Lagrangian methods [58] , [57] studied by the team of M. Falcone (La Sapienza, Rome), the high order schemes WENO,
ENO, Discrete galerkin introduced by S. Osher, C.-W. Shu, E. Harten
[61] , [62] , [63] , [71] , and also the schemes on nonregular grids by
R. Abgrall [37] , [36] .
All these schemes rely on finite differences or/and interpolation techniques
which lead to numerical diffusions. Hence, the
numerical solution is unsatisfying for long time approximations even
in the continuous case.

One of the (nonmonotone) schemes for solving the HJB equation is based on the Ultrabee algorithm proposed, in the case of advection equation with constant velocity, by Roe [74] and recently revisited by Després-Lagoutière [54] , [53] . The numerical results on several academic problems show the relevance of the antidiffusive schemes. However, the theoretical study of the convergence is a difficult question and is only partially done.

*Optimal stochastic control problems* occur when the dynamical system is
uncertain. A decision typically has to be taken at each time, while
realizations of future events are unknown (but some information is given
on their distribution of probabilities).
In particular, problems of economic nature deal with large uncertainties
(on prices, production and demand).
Specific examples are the portfolio selection problems in a market with risky
and non-risky assets, super-replication with uncertain volatility,
management of power resources (dams, gas).
Air traffic control is another example of such problems.

*Nonsmoothness of the value function*.
Sometimes the value function is smooth (e.g. in the case
of Merton's portfolio problem, Oksendal [76] )
and the associated HJB equation can be solved explicitly.
Still, the value function is not smooth enough to satisfy the
HJB equation in the classical sense. As for the deterministic
case, the notion of viscosity solution provides a convenient framework for
dealing with the lack of smoothness, see Pham [72] ,
that happens also to be
well adapted to the study of discretization errors for numerical
discretization schemes [65] , [41] .

*Numerical approximation for optimal stochastic control problems*.
The numerical discretization of second order HJB equations was the subject of several contributions.
The book of Kushner-Dupuis [66] gives a complete synthesis on the chain Markov schemes
(i.e Finite Differences, semi-Lagrangian, Finite Elements, ...).
Here a main difficulty of these equations comes from the fact that the second order operator
(i.e. the diffusion term) is not uniformly elliptic and can be degenerated.
Moreover, the diffusion term (covariance matrix) may change direction at any space point and at any time (this matrix is associated the dynamics volatility).

For solving stochastic control problems, we studied the so-called Generalized Finite Differences (GFD), that allow to choose at any node, the stencil approximating the diffusion matrix up to a certain threshold [47] . Determining the stencil and the associated coefficients boils down to a quadratic program to be solved at each point of the grid, and for each control. This is definitely expensive, with the exception of special structures where the coefficients can be computed at low cost. For two dimensional systems, we designed a (very) fast algorithm for computing the coefficients of the GFD scheme, based on the Stern-Brocot tree [46] .