l 1-l 2 regularization of split feasibility problems

Multilinear Model Estimation with L 2-Regularization

Many challenging computer vision problems can be formulated as a multilinear model. Classical methods like principal component analysis use singular value decomposition to infer model parameters. Although it can solve a given problem easily if all measurements are known this prerequisite is usually violated for computer vision applications. In the current work, a standard tool to estimate singu...

Robust Solutions to l 1 , l 2 , and l 1 Uncertain LinearApproximation Problems using Convex Optimization 1

We present minimax and stochastic formulations of some linear approximation problems with uncertain data in R n equipped with the Euclidean (l2), Absolute-sum (l1) or Chebyshev (l1) norms. We then show that these problems can be solved using convex optimization. Our results parallel and extend the work of El-Ghaoui and Lebret on robust least squares 3], and the work of Ben-Tal and Nemirovski on...

SOR - and Jacobi - type iterative methods for solving l 1 - l 2 problems by way of

We present an SOR-type algorithm and a Jacobi-type algorithm that can effectively be applied to the `1-`2 problem by exploiting its special structure. The algorithms are globally convergent and can be implemented in a particularly simple manner. Relations with coordinate minimization methods are discussed.

Jacobi - type iterative methods for solving l 1 - l 2 problems by way of Fenchel duality

We present an SOR-type algorithm and a Jacobi-type algorithm that can effectively be applied to the `1-`2 problem by exploiting its special structure. The algorithms are globally convergent and can be implemented in a particularly simple manner. Relations with coordinate minimization methods are discussed.

Calculating the Galois Group of L 1 (l 2 (y)) = 0, L 1 ; L 2 Completely Reducible Operators