Regularization by dynamic programming
نویسندگان
چکیده
We investigate continuous regularization methods for linear inverse problems of static and dynamic type. These methods are based on dynamic programming approaches for linear quadratic optimal control problems. We prove regularization properties and also obtain rates of convergence for our methods. A numerical example concerning a dynamical electrical impedance tomography (EIT) problem is used to illustrate the theoretical results.
منابع مشابه
Dual reciprocity BEM and dynamic programming filter for inverse elastodynamic problems
This paper presents the first coupling application of the dual reciprocity BEM (DRBEM) and dynamic programming filter to inverse elastodynamic problem. The DRBEM is the only BEM method, which does not require domain discretization for general linear and nonlinear dynamic problems. Since the size of numerical discretization system has a great effect on the computing effort of recursive or iterat...
متن کاملTechnical Note: Algorithms for Optimal Dyadic Decision Trees
A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, replacing the dynamic programming alg...
متن کاملLarge-scale Extended Linear-quadratic Programming and Multistage Optimization
Optimization problems in discrete time can be modeled more flexibly by extended linearquadratic programming than by traditional linear or quadratic programming, because penalties and other expressions that may substitute for constraints can readily be incorporated and dualized. At the same time, dynamics can be written with state vectors as in dynamic programming and optimal control. This sugge...
متن کاملPortfolio Optimization under Partial Information with Expert Opinions: a Dynamic Programming Approach*
This paper investigates optimal portfolio strategies in a market where the drift is driven by an unobserved Markov chain. Information on the state of this chain is obtained from stock prices and expert opinions in the form of signals at random discrete time points. As in Frey et al. (2012), Int. J. Theor. Appl. Finance, 15, No. 1, we use stochastic filtering to transform the original problem in...
متن کاملAn Optimal Tax Relief Policy with Aligning Markov Chain and Dynamic Programming Approach
Abstract In this paper, Markov chain and dynamic programming were used to represent a suitable pattern for tax relief and tax evasion decrease based on tax earnings in Iran from 2005 to 2009. Results, by applying this model, showed that tax evasion were 6714 billion Rials**. With 4% relief to tax payers and by calculating present value of the received tax, it was reduced to 3108 billion Rials. ...
متن کامل