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andrewrosemberg merged 1 commit into from
Sep 4, 2025
Merged

Fix Class 2 documentation links to use full website URLs #39

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12 changes: 6 additions & 6 deletions class02/class02.md
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Expand Up @@ -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

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14 changes: 7 additions & 7 deletions class02/overview.md
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Expand Up @@ -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
Expand All @@ -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

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