77 citations · 318 across the 57 of their papers we have counts for
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Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria
Dmitriy Drusvyatskiy, Alexander D. Ioffe, Adrian S. Lewis
We consider optimization algorithms that successively minimize simple Taylor-like models of the objective function. Methods of Gauss-Newton type for minimizing the composition of a…
Efficient quadratic penalization through the partial minimization technique
Aleksandr Y. Aravkin, Dmitriy Drusvyatskiy, Tristan van Leeuwen
Common computational problems, such as parameter estimation in dynamic models and PDE constrained optimization, require data fitting over a set of auxiliary parameters subject to p…
Efficiency of minimizing compositions of convex functions and smooth maps
Dmitriy Drusvyatskiy, Courtney Paquette
We consider global efficiency of algorithms for minimizing a sum of a convex function and a composition of a Lipschitz convex function with a smooth map. The basic algorithm we rel…
An optimal first order method based on optimal quadratic averaging
Dmitriy Drusvyatskiy, Maryam Fazel, Scott Roy
In a recent paper, Bubeck, Lee, and Singh introduced a new first order method for minimizing smooth strongly convex functions. Their geometric descent algorithm, largely inspired b…
Error bounds, quadratic growth, and linear convergence of proximal methods
Dmitriy Drusvyatskiy, Adrian S. Lewis
The proximal gradient algorithm for minimizing the sum of a smooth and a nonsmooth convex function often converges linearly even without strong convexity. One common reason is that…
The Euclidean Distance Degree of Orthogonally Invariant Matrix Varieties
Dmitriy Drusvyatskiy, Hon-Leung Lee, Giorgio Ottaviani +1
We show that the Euclidean distance degree of a real orthogonally invariant matrix variety equals the Euclidean distance degree of its restriction to diagonal matrices. We illustra…