1,591 research outputs found
Embedded techniques for choosing the parameter in Tikhonov regularization
This paper introduces a new strategy for setting the regularization parameter
when solving large-scale discrete ill-posed linear problems by means of the
Arnoldi-Tikhonov method. This new rule is essentially based on the discrepancy
principle, although no initial knowledge of the norm of the error that affects
the right-hand side is assumed; an increasingly more accurate approximation of
this quantity is recovered during the Arnoldi algorithm. Some theoretical
estimates are derived in order to motivate our approach. Many numerical
experiments, performed on classical test problems as well as image deblurring
are presented
Projected Newton Method for noise constrained Tikhonov regularization
Tikhonov regularization is a popular approach to obtain a meaningful solution
for ill-conditioned linear least squares problems. A relatively simple way of
choosing a good regularization parameter is given by Morozov's discrepancy
principle. However, most approaches require the solution of the Tikhonov
problem for many different values of the regularization parameter, which is
computationally demanding for large scale problems. We propose a new and
efficient algorithm which simultaneously solves the Tikhonov problem and finds
the corresponding regularization parameter such that the discrepancy principle
is satisfied. We achieve this by formulating the problem as a nonlinear system
of equations and solving this system using a line search method. We obtain a
good search direction by projecting the problem onto a low dimensional Krylov
subspace and computing the Newton direction for the projected problem. This
projected Newton direction, which is significantly less computationally
expensive to calculate than the true Newton direction, is then combined with a
backtracking line search to obtain a globally convergent algorithm, which we
refer to as the Projected Newton method. We prove convergence of the algorithm
and illustrate the improved performance over current state-of-the-art solvers
with some numerical experiments
Numerical analysis of least squares and perceptron learning for classification problems
This work presents study on regularized and non-regularized versions of
perceptron learning and least squares algorithms for classification problems.
Fr'echet derivatives for regularized least squares and perceptron learning
algorithms are derived. Different Tikhonov's regularization techniques for
choosing the regularization parameter are discussed. Decision boundaries
obtained by non-regularized algorithms to classify simulated and experimental
data sets are analyzed
Online Local Volatility Calibration by Convex Regularization with Morozov's Principle and Convergence Rates
We address the inverse problem of local volatility surface calibration from
market given option prices. We integrate the ever-increasing flow of option
price information into the well-accepted local volatility model of Dupire. This
leads to considering both the local volatility surfaces and their corresponding
prices as indexed by the observed underlying stock price as time goes by in
appropriate function spaces. The resulting parameter to data map is defined in
appropriate Bochner-Sobolev spaces. Under this framework, we prove key
regularity properties. This enable us to build a calibration technique that
combines online methods with convex Tikhonov regularization tools. Such
procedure is used to solve the inverse problem of local volatility
identification. As a result, we prove convergence rates with respect to noise
and a corresponding discrepancy-based choice for the regularization parameter.
We conclude by illustrating the theoretical results by means of numerical
tests.Comment: 23 pages, 5 figure
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