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Multiplicateur de Lagrange

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Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Nicolas Henri Bernard Flammarion

We consider minimizing a nonconvex, smooth function

f

on a Riemannian manifold

\mathcal{M}

. We show that a perturbed version of Riemannian gradient descent algorithm converges to a second-order stationary point (and hence is able to escape saddle point ...

2019

Multiplicateur de Lagrange

Graph Chatbot

Chattez avec Graph Search

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Gaussian Process Regression for Maximum Entropy Distribution

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

A priori error for unilateral contact problems with Lagrange multipliers and isogeometric analysis

Isogeometric Mortar Coupling for Electromagnetic Problems

Coupling non-conforming discretizations of PDEs by spectral approximation of the Lagrange multiplier space

Learning Generalized Nash Equilibria in a Class of Convex Games

Safe Convex Learning under Uncertain Constraints

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

A saddle point approach to an optimal boundary control problem for steady Navier-Stokes equations

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

A saddle point approach to an optimal boundary control problem for steady Navier-Stokes equations

Gaussian Process Regression for Maximum Entropy Distribution

A priori error for unilateral contact problems with Lagrange multipliers and isogeometric analysis

Coupling non-conforming discretizations of PDEs by spectral approximation of the Lagrange multiplier space

Safe Convex Learning under Uncertain Constraints

Learning Generalized Nash Equilibria in a Class of Convex Games

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Isogeometric Mortar Coupling for Electromagnetic Problems

Second-order guarantees in centralized, federated and decentralized nonconvex optimization