3,483 research outputs found
Nonlinear Parabolic Equations arising in Mathematical Finance
This survey paper is focused on qualitative and numerical analyses of fully
nonlinear partial differential equations of parabolic type arising in financial
mathematics. The main purpose is to review various non-linear extensions of the
classical Black-Scholes theory for pricing financial instruments, as well as
models of stochastic dynamic portfolio optimization leading to the
Hamilton-Jacobi-Bellman (HJB) equation. After suitable transformations, both
problems can be represented by solutions to nonlinear parabolic equations.
Qualitative analysis will be focused on issues concerning the existence and
uniqueness of solutions. In the numerical part we discuss a stable
finite-volume and finite difference schemes for solving fully nonlinear
parabolic equations.Comment: arXiv admin note: substantial text overlap with arXiv:1603.0387
Relaxation Methods for Mixed-Integer Optimal Control of Partial Differential Equations
We consider integer-restricted optimal control of systems governed by
abstract semilinear evolution equations. This includes the problem of optimal
control design for certain distributed parameter systems endowed with multiple
actuators, where the task is to minimize costs associated with the dynamics of
the system by choosing, for each instant in time, one of the actuators together
with ordinary controls. We consider relaxation techniques that are already used
successfully for mixed-integer optimal control of ordinary differential
equations. Our analysis yields sufficient conditions such that the optimal
value and the optimal state of the relaxed problem can be approximated with
arbitrary precision by a control satisfying the integer restrictions. The
results are obtained by semigroup theory methods. The approach is constructive
and gives rise to a numerical method. We supplement the analysis with numerical
experiments
Asymptotic Stability of POD based Model Predictive Control for a semilinear parabolic PDE
In this article a stabilizing feedback control is computed for a semilinear
parabolic partial differential equation utilizing a nonlinear model predictive
(NMPC) method. In each level of the NMPC algorithm the finite time horizon open
loop problem is solved by a reduced-order strategy based on proper orthogonal
decomposition (POD). A stability analysis is derived for the combined POD-NMPC
algorithm so that the lengths of the finite time horizons are chosen in order
to ensure the asymptotic stability of the computed feedback controls. The
proposed method is successfully tested by numerical examples
Optimal shape and location of sensors for parabolic equations with random initial data
In this article, we consider parabolic equations on a bounded open connected
subset of . We model and investigate the problem of optimal
shape and location of the observation domain having a prescribed measure. This
problem is motivated by the question of knowing how to shape and place sensors
in some domain in order to maximize the quality of the observation: for
instance, what is the optimal location and shape of a thermometer? We show that
it is relevant to consider a spectral optimal design problem corresponding to
an average of the classical observability inequality over random initial data,
where the unknown ranges over the set of all possible measurable subsets of
of fixed measure. We prove that, under appropriate sufficient spectral
assumptions, this optimal design problem has a unique solution, depending only
on a finite number of modes, and that the optimal domain is semi-analytic and
thus has a finite number of connected components. This result is in strong
contrast with hyperbolic conservative equations (wave and Schr\"odinger)
studied in [56] for which relaxation does occur. We also provide examples of
applications to anomalous diffusion or to the Stokes equations. In the case
where the underlying operator is any positive (possible fractional) power of
the negative of the Dirichlet-Laplacian, we show that, surprisingly enough, the
complexity of the optimal domain may strongly depend on both the geometry of
the domain and on the positive power. The results are illustrated with several
numerical simulations
A Probabilistic Numerical Method for Fully Nonlinear Parabolic PDEs
We consider the probabilistic numerical scheme for fully nonlinear PDEs
suggested in \cite{cstv}, and show that it can be introduced naturally as a
combination of Monte Carlo and finite differences scheme without appealing to
the theory of backward stochastic differential equations. Our first main result
provides the convergence of the discrete-time approximation and derives a bound
on the discretization error in terms of the time step. An explicit
implementable scheme requires to approximate the conditional expectation
operators involved in the discretization. This induces a further Monte Carlo
error. Our second main result is to prove the convergence of the latter
approximation scheme, and to derive an upper bound on the approximation error.
Numerical experiments are performed for the approximation of the solution of
the mean curvature flow equation in dimensions two and three, and for two and
five-dimensional (plus time) fully-nonlinear Hamilton-Jacobi-Bellman equations
arising in the theory of portfolio optimization in financial mathematics
Controlled diffusion processes
This article gives an overview of the developments in controlled diffusion
processes, emphasizing key results regarding existence of optimal controls and
their characterization via dynamic programming for a variety of cost criteria
and structural assumptions. Stochastic maximum principle and control under
partial observations (equivalently, control of nonlinear filters) are also
discussed. Several other related topics are briefly sketched.Comment: Published at http://dx.doi.org/10.1214/154957805100000131 in the
Probability Surveys (http://www.i-journals.org/ps/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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