Totally Asynchronous Large-Scale Quadratic Programming: Regularization, Convergence Rates, and Parameter Selection

An Algorithm for Large-Scale Quadratic Programming

We are particularly concerned in solving (1.1) when n is large and the vectors a, and matrix H are sparse. We do not restrict H to being positive (semi-)definite and consequently are content with finding local solutions to (1.1). Of course, for many classes of problem, it is known a priori that any local solution is a global one. Our method is fundamentally related to that proposed by Fletcher ...

Large-scale decomposition for successive quadratic programming

Large-scale Inversion of Magnetic Data Using Golub-Kahan Bidiagonalization with Truncated Generalized Cross Validation for Regularization Parameter Estimation

In this paper a fast method for large-scale sparse inversion of magnetic data is considered. The L1-norm stabilizer is used to generate models with sharp and distinct interfaces. To deal with the non-linearity introduced by the L1-norm, a model-space iteratively reweighted least squares algorithm is used. The original model matrix is factorized using the Golub-Kahan bidiagonalization that proje...

Large-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...

Scale Recognition, Regularization Parameter Selection, and Meyer's G Norm in Total Variation Regularization

We investigate how TV regularization naturally recognizes scale of individual image features and we show how perception of scale depends on the amount of regularization applied to the image We give an automatic method for nding the minimum value of the regularization parameter needed to remove all features below a user chosen threshold We explain the relation of Meyer s G norm to the perception...