Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Projected Gradient Descent: Convergence and Optimization
Graph Chatbot
Related lectures (32)
Previous
Page 3 of 4
Next
Recommender Systems: Matrix Factorization
Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.
Optimization Methods: Theory Discussion
Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.
Optimization with Constraints: KKT Conditions
Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.
Multivariable Control: System Theory and Applications
Covers system theory, classic feedback control, and applications in green building and natural gas refrigeration plants.
Offset-free tracking: Necessary conditions and Feedforward compensation
Explores offset-free tracking in multivariable control, covering necessary conditions and feedforward compensation to reject disturbances and achieve constant setpoints.
Kalman Filter: Linearized vs Extended
Explores the linearized and extended Kalman Filters, illustrating their application in nonlinear systems and the estimation of unknown parameters.
Suspension Optimisation: Performance Analysis and Optimization
Explores the design and optimization of car suspensions for improved performance.
Exact and Approximate Sampling in Multivariable Control
Explores exact and approximate sampling in multivariable control systems, discussing stability, eigenvalues, and system properties.
Optimization methods
Covers optimization methods, focusing on gradient methods and line search techniques.
Controlled Stochastic Processes
Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.