Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates

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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 ...

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Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates

Chattez avec Graph Search

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A Newton Frank-Wolfe method for constrained self-concordant minimization

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Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Logical Team Q-learning: An approach towards factored policies in cooperative MARL

Light Path Gradients for Forward and Inverse Rendering

A first-order primal-dual method with adaptivity to local smoothness

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Parameter Estimation of Three-Phase Untransposed Short Transmission Lines from Synchrophasor Measurements

Coordinate Difference Matrices

Adaptive Gradient Descent without Descent