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A first-order primal-dual method with adaptivity to local smoothness
Maria-Luiza Vladarean · Yura Malitsky · Volkan Cevher

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @
We consider the problem of finding a saddle point for the convex-concave objective $\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)$, where $f$ is a convex function with locally Lipschitz gradient and $g$ is convex and possibly non-smooth. We propose an adaptive version of the Condat-Vũ algorithm, which alternates between primal gradient steps and dual proximal steps. The method achieves stepsize adaptivity through a simple rule involving $\|A\|$ and the norm of recently computed gradients of $f$. Under standard assumptions, we prove an $\mathcal{O}(k^{-1})$ ergodic convergence rate. Furthermore, when $f$ is also locally strongly convex and $A$ has full row rank we show that our method converges with a linear rate. Numerical experiments are provided for illustrating the practical performance of the algorithm.

Author Information

Maria-Luiza Vladarean (Ecole Polytechnique Federale de Lausanne)

My fields of expertise are convex, non-convex and stochastic first-order methods. I'm also interested in the properties of ODE discretizations (specifically the pair GF/GD) and the connections between dynamical systems and optimization algorithms. I graduated with a Masters in Computer Science and a minor in Biocomputing from EPFL in 2016. I subsequently spent two years in the industry working as a Software Engineer at Amazon Video in London. At the end of 2018 I returned to EPFL to start my PhD in mathematical optimization, for which I've been lucky to receive the guidance of Prof. Nicolas Flammarion.

Yura Malitsky (Linköping University)
Volkan Cevher (EPFL)

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