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A weak convergence approach to the theory of large deviations
Formulation of Large Deviation Theory in Terms of the Laplace Principle. First Example: Sanov's Theorem. Second Example: Mogulskii's Theorem. Representation Formulas for Other Stochastic Processes.Expand
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Dynamical systems and variational inequalities
TLDR
The variational inequality problem has been utilized to formulate and study a plethora of competitive equilibrium problems in different disciplines, ranging from oligopolistic market equilibrium problems to traffic network equilibrium problems. Expand
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On Lipschitz continuity of the solution mapping to the Skorokhod problem
The solution m the Skorokhoci Problem defines a deieiminisiic mapping of paths that has been found to be useful in several areas of application. Typical uses of the mapping are construction andExpand
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A VARIATIONAL REPRESENTATION FOR POSITIVE FUNCTIONALS OF INFINITE DIMENSIONAL BROWNIAN MOTION
A variational representation for positive functionals of a Hilbert space valued Wiener process (W (·)) is proved. This representation is then used to prove a large deviations principle for the familyExpand
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A variational representation for certain functionals of Brownian motion
In this paper we show that the variational representation - log Ee -f(W) = inf E{1/2∫ 0 1 ∥v s ∥ 2 ds + f(w + ∫ 0 v s ds)} holds, where W is a standard d-dimensional Brownian motion, f is any boundedExpand
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Large deviations for infinite dimensional stochastic dynamical systems
The large deviations analysis of solutions to stochastic differential equations and related processes is often based on approximation. The construction and justification of the approximations can beExpand
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SDEs with Oblique Reflection on Nonsmooth Domains
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Variational problems on ows of di eomorphisms for image matching
TLDR
A food plate package having a bottom plate and a top cover for the temporary storage of prepared food products, the top cover being convex-oval, with support ribs integrally formed in the cover. Expand
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Minimax optimal control of stochastic uncertain systems with relative entropy constraints
TLDR
We introduce a new class of discrete time stochastic uncertain systems in which the uncertainty is described by a constraint on the relative entropy between a nominal noise distribution and the perturbed noise distribution. Expand
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