Lecture
Projected Gradient Descent and Quadratic Penalty
In course
DEMO: do proident minim adipisicing
Eiusmod nisi amet esse dolore sunt duis in culpa irure incididunt laboris. Consequat tempor sunt nostrud in adipisicing est eu velit id qui dolore veniam nulla ad. Ea minim ipsum consequat proident voluptate officia ea officia ea. Et quis est est dolore fugiat amet fugiat dolor tempor irure excepteur qui pariatur consectetur.
Description
This lecture covers Projected Gradient Descent (PGD) and Quadratic Penalty methods, focusing on the assumptions and convergence properties. It explains how PGD works under different conditions and how Quadratic Penalty methods can be used to solve optimization problems. The lecture also discusses the convergence of PGD and the optimality conditions for Quadratic Penalty methods.
Instructor
ipsum incididunt ea
Est velit elit aliquip nostrud dolore fugiat consectetur aliquip aute. Irure qui Lorem ad consequat ullamco irure excepteur duis amet eiusmod nostrud labore dolore. Cupidatat dolor reprehenderit velit quis et reprehenderit exercitation eu aliqua ullamco.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (39)
Quadratic Penalty Methods: Sound Problems
Explores Quadratic Penalty Methods for optimization with enforced constraints using penalty functions.
Quadratic Penalty Method: Finer Analysis
Covers the quadratic penalty method and augmented Lagrangian, including the setup and convergence of sequences.
Linear Programming: Weighted Bipartite Matching
Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.
Thermodynamic Properties: Equations and Models
Explains thermodynamic properties, equations of state, and mixture rules for energy systems modeling.
Optimisation in Energy Systems
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.