In this paper, we study the solving of gridless sparse optimization problem and its application to 3D image deconvolution. Based on recent works (Denoyelle et al, 2019) introducing Sliding Frank-Wolfe algorithm solve Beurling LASSO problem, introduce an accelerated algorithm, denoted BSFW, that preserves convergence properties, while removing most costly local descents. Besides, as BLASSO still...

In this paper, the online variants of the classical Frank-Wolfe algorithm are considered. We consider minimizing the regret with a stochastic cost. The online algorithms only require simple iterative updates and a non-adaptive step size rule, in contrast to the hybrid schemes commonly considered in the literature. Several new results are derived for convex and non-convex losses. With a strongly...

Any performance analysis based on stochastic simulation is subject to the errors inherent in misspecifying the modeling assumptions, particularly the input distributions. In situations with little support from data, we investigate the use of worst-case analysis to analyze these errors, by representing the partial, nonparametric knowledge of the input models via optimization constraints. We stud...

In this paper, the global optimization of a nonconvex objective function under linear and nonlinear differentiable constraints is studied, a reduced gradient and GRG descent methods with random perturbation is proposed and it is desired to establish the global convergence of the algorithm. Some numerical examples are also given by the problems of statistical, octagon, mixture, alkylation and po...

Inventory of differential units of a deteriorating item purchased in a lot and sold separately from two shops under a single management is considered. Here deterioration increases with time and demands are timeand price-dependent for fresh and deteriorated units respectively. For the fresh units, shortages are allowed and later partially-backlogged. For the deteriorated units, there are two sce...

We present several online algorithms with dimension-free regret bounds for general nonconvex quadratic losses by viewing them as functions in Reproducing Hilbert Kernel Spaces. In our work we adapt the Online Gradient Descent, Follow the Regularized Leader and the Conditional Gradient method meta algorithms for RKHS spaces and provide regret bounds in this setting. By analyzing them as algorith...