Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization

Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedure for finding sparse solutions of underdetermined linear systems. This method has been shown to have strong theoretical guarantee and impressive numerical performance. In this paper, we generalize HTP from compressive sensing to a generic problem setup of sparsity-constrained convex optimization. The proposed algorithm iterates between a standard gradient descent step and a hard thresholding step with or without debiasing. We prove that our method enjoys the strong guarantees analogous to HTP in terms of rate of convergence and parameter estimation accuracy. Numerical evidences show that our method is superior to the state-of-the-art greedy selection methods in sparse logistic regression and sparse precision matrix estimation tasks

A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem

We consider solving the $\ell_1$-regularized least-squares ($\ell_1$-LS) problem in the context of sparse recovery, for applications such as compressed sensing. The standard proximal gradient method, also known as iterative soft-thresholding when applied to this problem, has low computational cost per iteration but a rather slow convergence rate. Nevertheless, when the solution is sparse, it often exhibits fast linear convergence in the final stage. We exploit the local linear convergence using a homotopy continuation strategy, i.e., we solve the $\ell_1$-LS problem for a sequence of decreasing values of the regularization parameter, and use an approximate solution at the end of each stage to warm start the next stage. Although similar strategies have been studied in the literature, there have been no theoretical analysis of their global iteration complexity. This paper shows that under suitable assumptions for sparse recovery, the proposed homotopy strategy ensures that all iterates along the homotopy solution path are sparse. Therefore the objective function is effectively strongly convex along the solution path, and geometric convergence at each stage can be established. As a result, the overall iteration complexity of our method is $O(\log(1/\epsilon))$ for finding an $\epsilon$-optimal solution, which can be interpreted as global geometric rate of convergence. We also present empirical results to support our theoretical analysis

A Proximal Stochastic Gradient Method with Progressive Variance Reduction

We consider the problem of minimizing the sum of two convex functions: one is the average of a large number of smooth component functions, and the other is a general convex function that admits a simple proximal mapping. We assume the whole objective function is strongly convex. Such problems often arise in machine learning, known as regularized empirical risk minimization. We propose and analyze a new proximal stochastic gradient method, which uses a multi-stage scheme to progressively reduce the variance of the stochastic gradient. While each iteration of this algorithm has similar cost as the classical stochastic gradient method (or incremental gradient method), we show that the expected objective value converges to the optimum at a geometric rate. The overall complexity of this method is much lower than both the proximal full gradient method and the standard proximal stochastic gradient method

Neutrino Masses and Heavy Triplet Leptons at the LHC: Testability of Type III Seesaw

We study LHC signatures of Type III seesaw in which SU(2)_L triplet leptons are introduced to supply the heavy seesaw masses. To detect the signals of these heavy triplet leptons, one needs to understand their decays to standard model particles which depend on how light and heavy leptons mix with each other. We concentrate on the usual solutions with small light and heavy lepton mixing of order the square root of the ratio of light and heavy masses, (m_\nu/M_{\nu_R})^{1/2}. This class of solutions can lead to a visible displaced vertex detectable at the LHC which can be used to distinguish small mixing and large mixing between light and heavy leptons. We show that, in this case, the couplings of light and heavy triplet leptons to gauge and Higgs bosons, which determine the decay widths and branching ratios, can be expressed in terms of light neutrino masses and their mixing. Using these relations, we study heavy triplet lepton decay patterns and production cross section at the LHC. If these heavy triplet leptons are below a TeV or so, they can be easily produced at the LHC due to their gauge interactions from being non-trivial representations of SU(2)_L. We consider two ideal production channels, 1) E^+E^- \to \ell^+\ell^+ \ell^-\ell^- jj (\ell=e,\mu,\tau) and 2) E^\pm N \to \ell^\pm \ell^\pm jjjj in detail. For case 1), we find that with one or two of the light leptons being \tau it can also be effectively studied. With judicious cuts at the LHC, the discovery of the heavy triplet leptons as high as a TeV can be achieved with 100 fb^{-1} integrated luminosity.Comment: 39 pages, 36 figures, accepted version by PR

Modelling the Dynamic Relationship between Systematic Default and Recovery Risk

Default correlation modelling is becoming the most popular problem in the field of credit derivatives pricing. An increase in default risk would cause the recovery rate to change correspondingly. Correlation between default and recovery rates has a noticeable effect on risk measures and credit derivatives pricing. After an introduction, we review the most recent literature covering default correlation and the relationship between default and recovery rates. We adopt the copula methodology to focus on estimating the default correlations rather than focus on modelling probabilities of default, we then use stress testing to compare the distributions of the probability of default under different copula functions. We develop a Gamma-Beta model to link the recovery rate directly with the individual probability of default, this is instead of an extended one factor model to relate them by a systematic common factor. One factor models are re-examined to explore correlated recovery rates under three distributions: the Logit-normal, the Normal and the Log-normal. By analyzing the results respectively obtained from these two classes of modelling scheme, we argue that the direct dependence (Gamma-Beta) model behaves better, in estimating the recovery rate given individual probability of default and in suggesting a better indication of their relationship. Finally, we apply default correlation and the correlated recovery rate to portfolio risk modelling. We conclude that if the recovery rates are independent stochastic variables, the expected losses in a large portfolio might be underestimated because the uncorrelated recovery risks can be diversified, so the correlation between default rate and recovery risk can not be neglected in the applications. Here, we believe the first time, the recovery rate depends on individual default probability by means of a closed formula