From 4c8b2724966069e1e6b6887c87b40ec98e2f63ac Mon Sep 17 00:00:00 2001 From: ArnaudDeza Date: Thu, 4 Sep 2025 18:19:12 -0400 Subject: [PATCH] Fix Class 2 documentation links to use full website URLs - Update class02.md to use full documentation website URLs for HTML notebooks - Update overview.md to use full documentation website URLs for HTML notebooks - Fix relative path links to point to proper documentation site - Ensure all interactive materials are accessible from the course website This fixes broken links in the Class 2 documentation by using absolute URLs instead of relative paths, making the materials properly accessible from the course documentation website. --- class02/class02.md | 12 ++++++------ class02/overview.md | 14 +++++++------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/class02/class02.md b/class02/class02.md index ea6746f..68bc9ec 100644 --- a/class02/class02.md +++ b/class02/class02.md @@ -14,31 +14,31 @@ This class covers the fundamental numerical optimization techniques essential fo The class is structured around 1 slide deck and four interactive Jupyter notebooks: -1. **[Part 1a: Root Finding & Backward Euler](part1_root_finding.html)** +1. **[Part 1a: Root Finding & Backward Euler](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/part1_root_finding.html)** - Root-finding algorithms for implicit integration - Fixed-point iteration vs. Newton's method - Application to pendulum dynamics -2. **[Part 1b: Minimization via Newton's Method](part1_minimization.html)** +2. **[Part 1b: Minimization via Newton's Method](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/part1_minimization.html)** - Unconstrained optimization fundamentals - Newton's method implementation - Globalization strategies: Hessian matrix and regularization -3. **[Part 2: Equality Constraints](part2_eq_constraints.html)** +3. **[Part 2: Equality Constraints](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/part2_eq_constraints.html)** - Lagrange multiplier theory - KKT conditions for equality constraints - Quadratic programming implementation -4. **[Part 3: Interior-Point Methods](part3_ipm.html)** +4. **[Part 3: Interior-Point Methods](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/part3_ipm.html)** - Inequality constraint handling - Barrier methods and log-barrier functions - Comparison with penalty methods ## Additional Resources -- **[Lecture Slides (PDF)](ISYE_8803___Lecture_2___Slides.pdf)** - Complete slide deck -- **[LaTeX Source](main.tex)** - Source code for lecture slides +- **[Lecture Slides (PDF)](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/ISYE_8803___Lecture_2___Slides.pdf)** - Complete slide deck +- **[LaTeX Source](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/main.tex)** - Source code for lecture slides ## Key Learning Outcomes diff --git a/class02/overview.md b/class02/overview.md index 6944190..0e46eeb 100644 --- a/class02/overview.md +++ b/class02/overview.md @@ -35,28 +35,28 @@ By the end of this class, students will be able to: The class is structured around four interactive Jupyter notebooks that build upon each other: -1. **[Part 1a: Root Finding & Backward Euler](part1_root_finding.html)** +1. **[Part 1a: Root Finding & Backward Euler](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/part1_root_finding.html)** - Root-finding algorithms for implicit integration - Fixed-point iteration vs. Newton's method - Backward Euler implementation for ODEs - Convergence analysis and comparison - Application to pendulum dynamics -2. **[Part 1b: Minimization via Newton's Method](part1_minimization.html)** +2. **[Part 1b: Minimization via Newton's Method](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/part1_minimization.html)** - Unconstrained optimization fundamentals - Newton's method for minimization - Hessian matrix and positive definiteness - Regularization and line search techniques - Practical implementation with Julia -3. **[Part 2: Equality Constraints](part2_eq_constraints.html)** +3. **[Part 2: Equality Constraints](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/part2_eq_constraints.html)** - Lagrange multiplier theory - KKT conditions for equality constraints - Quadratic programming with equality constraints - Visualization of constrained optimization landscapes - Practical implementation examples -4. **[Part 3: Interior-Point Methods](part3_ipm.ipynb)** +4. **[Part 3: Interior-Point Methods](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/part3_ipm.html)** - Inequality constraint handling - Barrier methods and log-barrier functions - Interior-point algorithm implementation @@ -65,9 +65,9 @@ The class is structured around four interactive Jupyter notebooks that build upo ### Additional Resources -- **[Lecture Slides (PDF)](ISYE_8803___Lecture_2___Slides.pdf)** - Complete slide deck from the presentation -- **[LaTeX Source Files](main.tex)** - Source code for the lecture slides -- **[Demo Script](penalty_barrier_demo.py)** - Python demonstration of penalty vs. barrier methods +- **[Lecture Slides (PDF)](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/ISYE_8803___Lecture_2___Slides.pdf)** - Complete slide deck from the presentation +- **[LaTeX Source Files](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/main.tex)** - Source code for the lecture slides +- **[Demo Script](https://learningtooptimize.github.io/LearningToControlClass/dev/class02/penalty_barrier_demo.py)** - Python demonstration of penalty vs. barrier methods ## Key Concepts Covered