479 research outputs found
An Infeasible-Point Subgradient Method Using Adaptive Approximate Projections
We propose a new subgradient method for the minimization of nonsmooth convex
functions over a convex set. To speed up computations we use adaptive
approximate projections only requiring to move within a certain distance of the
exact projections (which decreases in the course of the algorithm). In
particular, the iterates in our method can be infeasible throughout the whole
procedure. Nevertheless, we provide conditions which ensure convergence to an
optimal feasible point under suitable assumptions. One convergence result deals
with step size sequences that are fixed a priori. Two other results handle
dynamic Polyak-type step sizes depending on a lower or upper estimate of the
optimal objective function value, respectively. Additionally, we briefly sketch
two applications: Optimization with convex chance constraints, and finding the
minimum l1-norm solution to an underdetermined linear system, an important
problem in Compressed Sensing.Comment: 36 pages, 3 figure
On the convergence of mirror descent beyond stochastic convex programming
In this paper, we examine the convergence of mirror descent in a class of
stochastic optimization problems that are not necessarily convex (or even
quasi-convex), and which we call variationally coherent. Since the standard
technique of "ergodic averaging" offers no tangible benefits beyond convex
programming, we focus directly on the algorithm's last generated sample (its
"last iterate"), and we show that it converges with probabiility if the
underlying problem is coherent. We further consider a localized version of
variational coherence which ensures local convergence of stochastic mirror
descent (SMD) with high probability. These results contribute to the landscape
of non-convex stochastic optimization by showing that (quasi-)convexity is not
essential for convergence to a global minimum: rather, variational coherence, a
much weaker requirement, suffices. Finally, building on the above, we reveal an
interesting insight regarding the convergence speed of SMD: in problems with
sharp minima (such as generic linear programs or concave minimization
problems), SMD reaches a minimum point in a finite number of steps (a.s.), even
in the presence of persistent gradient noise. This result is to be contrasted
with existing black-box convergence rate estimates that are only asymptotic.Comment: 30 pages, 5 figure
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