This paper reveals that a common and central role, played in many error bound (EB)
conditions and a variety of gradient-type methods, is a residual measure operator. On one …

The condition number of a differentiable convex function, namely the ratio of its smoothness
to strong convexity constants, is closely tied to fundamental properties of the function. In …

In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE)
method in which sample size is gradually increasing to quickly obtain a solution whose …

We consider the problem of minimizing the sum of an average of a large number of smooth
convex component functions and a possibly nonsmooth convex function that admits a simple …

This work presents a new optimization algorithm for empirical risk minimization. The
algorithm bridges the gap between first-and second-order methods by computing a search …

The condition number of a smooth convex function, namely the ratio of its smoothness to
strong convexity constants, is closely tied to fundamental properties of the function. In …

Le present expose decrit une methode experimentale, concue dans le but de determiner les
caracteristiques de fuite d'air des murs exterieurs d'un Bdifice. Cette methode consiste B …

In this work1, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE)
method in which sample size is gradually increasing to quickly obtain a solution whose …

This thesis focuses on three themes related to the mathematical theory of first-order methods
for convex minimization: acceleration, conditioning, and rescaling. Chapters 1 and 2 explore …

Abstract 1 1 Dual Free Adaptive Mini-batch SDCA for Empirical Risk Minimization 3 1.1
Introduction..................................... 3 1.1. 1 Contributions................................ 5 1.1. 2 …