اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدStochastic Maximum Principle for a PDEs with noise and control on the boundary
458
0
0.0
(
0
)
نشر من قبل
Giuseppina Guatteri
تاريخ النشر
2011
مجال البحث
الهندسة المعلوماتية
والبحث باللغة
English
تأليف
Giuseppina Guatteri
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In this paper we prove necessary conditions for optimality of a stochastic control problem for a class of stochastic partial differential equations that is controlled through the boundary. This kind of problems can be interpreted as a stochastic control problem for an evolution system in an Hilbert space. The regularity of the solution of the adjoint equation, that is a backward stochastic equation in infinite dimension, plays a crucial role in the formulation of the maximum principle.
قيم البحث
اقرأ أيضاً
In this work we first present the existence, uniqueness and regularity of the strong solution of the tidal dynamics model perturbed by Levy noise. Monotonicity arguments have been exploited in the proofs. We then formulate a martingale problem of Str
oock and Varadhan associated to an initial value control problem and establish existence of optimal controls.
We consider the stochastic optimal control problem for the dynamical system of the stochastic differential equation driven by a local martingale with a spatial parameter. Assuming the convexity of the control domain, we obtain the stochastic maximum
principle as the necessary condition for an optimal control, and we also prove its sufficiency under proper conditions. The stochastic linear quadratic problem in this setting is also discussed.
Consider the stochastic heat equation $partial_tu=mathscr{L}u+lambdasigma(u)xi$, where $mathscr{L}$ denotes the generator of a L{e}vy process on a locally compact Hausdorff Abelian group $G$, $sigma:mathbf{R}tomathbf{R}$ is Lipschitz continuous, $lam
bdagg1$ is a large parameter, and $xi$ denotes space-time white noise on $mathbf{R}_+times G$. The main result of this paper contains a near-dichotomy for the (expected squared) energy $mathrm{E}(|u_t|_{L^2(G)}^2)$ of the solution. Roughly speaking, that dichotomy says that, in all known cases where $u$ is intermittent, the energy of the solution behaves generically as $exp{operatorname {const}cdot,lambda^2}$ when $G$ is discrete and $geexp{operatorname {const}cdot,lambda^4}$ when $G$ is connected.
In this paper, we aim to solve the high dimensional stochastic optimal control problem from the view of the stochastic maximum principle via deep learning. By introducing the extended Hamiltonian system which is essentially an FBSDE with a maximum co
ndition, we reformulate the original control problem as a new one. Three algorithms are proposed to solve the new control problem. Numerical results for different examples demonstrate the effectiveness of our proposed algorithms, especially in high dimensional cases. And an important application of this method is to calculate the sub-linear expectations, which correspond to a kind of fully nonlinear PDEs.
In this paper, the optimal control problem of neutral stochastic functional differential equation (NSFDE) is discussed. A class of so-called neutral backward stochastic functional equations of Volterra type (VNBSFEs) are introduced as the adjoint equ
ation. The existence and uniqueness of VNBSFE is established. The Pontryagin maximum principle is constructed for controlled NSFDE with Lagrange type cost functional.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
