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Found 6 papers, 2 papers with code
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
In this paper, we study how the Koopman operator framework can be combined with kernel methods to effectively control nonlinear dynamical systems.
Stochastic Zeroth order Descent with Structured Directions
For smooth convex functions we prove almost sure convergence of the iterates and a convergence rate on the function values of the form $O(d/l k^{-c})$ for every $c<1/2$, which is arbitrarily close to the one of Stochastic Gradient Descent (SGD) in terms of number of iterations.
Iterative regularization for low complexity regularizers
Our approach is based on a primal-dual algorithm of which we analyze convergence and stability properties, even in the case where the original problem is unfeasible.
A Stochastic Bregman Primal-Dual Splitting Algorithm for Composite Optimization
Under slightly stricter assumptions, we show almost sure weak convergence of the pointwise iterates to a saddle point.
Iterative regularization for convex regularizers
We study iterative regularization for linear models, when the bias is convex but not necessarily strongly convex.
Inexact and Stochastic Generalized Conditional Gradient with Augmented Lagrangian and Proximal Step
In this paper we propose and analyze inexact and stochastic versions of the CGALP algorithm developed in the authors' previous paper, which we denote ICGALP, that allows for errors in the computation of several important quantities.
