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Given a closed convex set A in a Banach space X, motivated by the continuity and Fréchet differentiability of A introduced, respectively, in [D. Gale and V. Klee, Math. Scand., 7 (1959), pp. 379-391] and [X. Y. Zheng, SIAM J. Optim., 30 (2020), pp. 490-512], this paper considers the Cp-differentiability, subdifferentiability, and G\^ateaux differentiability of A. Using the technique of variational analysis, it is proved that A is Cp-differentiable (resp., subdifferentiable or G\^ateaux differentiable) if and only if for every continuous convex function f : X → R with infx∈A f(x) > infx∈X f(x) the corresponding constrained optimization problem PA(f) is 2/p-order-well-posed solvable (resp., generalized well-posed solvable or weak well-posed solvable). It is also proved that if the conjugate function f* of a continuous convex function f on X is Cp-differentiable on dom(f*), then for every closed convex set A in X with infx∈A f(x) > -∞ the corresponding optimization problem PA(f) is 2/p-order-well-posed solvable. As a byproduct, every constrained convex optimization problem with a strongly convex quadratic objective function is proved to be globally second-order-well-posed solvable. Our main results are new even in the case of finite dimensional spaces. [ABSTRACT FROM AUTHOR]

We analyze the maximal growth of trajectories of discrete-time linear switching system, i.e., controlled linear systems with the control set being an arbitrary compact set of matrices. This is done by applying the optimal convex Lyapunov function called the Barabanov norm, which provides a very refined analysis of trajectories. Until recently that notion remained rather theoretical apart from special cases. In 2015 N. Guglielmi and M. Zennaro [SIAM J. Matrix Anal. Appl., 36 (2015), pp. 634-655] showed that many systems possess at least one efficiently computed Barabanov norm. In this paper we classify all possible Barabanov norms and prove that, under mild assumptions, which can be verified algorithmically, those norms are unique and are either piecewise-linear or piecewise-quadratic. For some narrow classes of systems, there are more complicated Barabanov norms, but they can still be classified and constructed. Using those results we find all trajectories of the fastest growth. Examples and numerical results are provided. [ABSTRACT FROM AUTHOR]

We consider a variable metric and inexact version of the fast iterative soft-thresholding algorithm (FISTA) type algorithm considered in [L. Calatroni and A. Chambolle, SIAM J. Optim., 29 (2019), pp. 1772-1798; A. Chambolle and T. Pock, Acta Numer., 25 (2016), pp. 161-319] for the minimization of the sum of two (possibly strongly) convex functions. The proposed algorithm is combined with an adaptive (nonmonotone) backtracking strategy, which allows for the adjustment of the algorithmic step-size along the iterations in order to improve the convergence speed. We prove a linear convergence result for the function values, which depends on both the strong convexity moduli of the two functions and the upper and lower bounds on the spectrum of the variable metric operators. We validate the proposed algorithm, named Scaled Adaptive GEneralized FISTA (SAGE-FISTA), on exemplar image denoising and deblurring problems where edge-preserving total variation (TV) regularization is combined with Kullback-Leibler-type fidelity terms, as is common in applications where signal-dependent Poisson noise is assumed in the data. [ABSTRACT FROM AUTHOR]