Search Results for author:
Found 7 papers, 0 papers with code
Convergence of the Expectation-Maximization Algorithm Through Discrete-Time Lyapunov Stability Theory
Furthermore, we propose to assess its convergence as asymptotic stability in the sense of Lyapunov.
Analysis of a Generalized Expectation-Maximization Algorithm for Gaussian Mixture Models: A Control Systems Perspective
The Expectation-Maximization (EM) algorithm is one of the most popular methods used to solve the problem of parametric distribution-based clustering in unsupervised learning.
Finite-Time Convergence of Continuous-Time Optimization Algorithms via Differential Inclusions
In this paper, we propose two discontinuous dynamical systems in continuous time with guaranteed prescribed finite-time local convergence to strict local minima of a given cost function.
A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm
Out of the recent advances in systems and control (S\&C)-based analysis of optimization algorithms, not enough work has been specifically dedicated to machine learning (ML) algorithms and its applications.
First-Order Optimization Inspired from Finite-Time Convergent Flows
In this paper, we investigate the performance of two first-order optimization algorithms, obtained from forward Euler discretization of finite-time optimization flows.
First-Order Optimization Algorithms via Discretization of Finite-Time Convergent Flows
In this paper, we investigate in the context of deep neural networks, the performance of several discretization algorithms for two first-order finite-time optimization flows.
Finite-Time Convergence in Continuous-Time Optimization
In this paper, we investigate a Lyapunov-like differential inequality that allows us to establish finite-time stability of a continuous-time state-space dynamical system represented via a multivariate ordinary differential equation or differential inclusion.
