Nonlinear Least Squares Interactive

Introduces iterative algorithms for solving least squares problems where the model is nonlinear. Nonlinear Least Squares Interactive turns that idea into something you can manipulate directly and watch respond.

Introduces iterative algorithms for solving least squares problems where the model is nonlinear. It focuses on Gauss-Newton algorithm, Levenberg-Marquardt algorithm, Augmented Lagrangian, Penalty algorithm from the “Nonlinear Least Squares” section.

The sliders change the key inputs; you can drag elements directly on the canvas; the “Gauss-Newton Step”, “Levenberg-Marquardt Step”, “Reset” buttons run actions or reset the demo. Every change updates the visualization in real time, so you can see exactly how each variable affects the outcome.

This course provides a practical introduction to linear algebra, focusing on the core concepts of vectors, matrices, and least squares methods. It emphasizes real-world applications such as machine learning, control sys… This demo is the interactive piece for the “Nonlinear Least Squares” section. Open the full Applied Linear Algebra and Least Squares course wiki at https://www.getlykke.com/explore/public/applied-linear-algebra-and-least-squares-f2e383ce-d1e8-4a0a-af8a-f21feb45dad0 for notes, flashcards, quizzes and the other demos.

You'll build intuition for Gauss-Newton algorithm, Levenberg-Marquardt algorithm, Augmented Lagrangian, Penalty algorithm — and, crucially, see how they behave when you change the inputs, which is hard to get from a textbook or lecture on Applied Linear Algebra and Least Squares alone.