clean up md files

Author: ArnaudDeza

class02/class02.md

@@ -12,18 +12,19 @@ This class covers the fundamental numerical optimization techniques essential fo
 
 ## Interactive Materials
 
-The class is structured around four interactive Jupyter notebooks:
+The class is structured around 1 slide deck and four interactive Jupyter notebooks:
 
-1. **[Part 1: Minimization via Newton's Method](part1_minimization.html)**
-   - Unconstrained optimization fundamentals
-   - Newton's method implementation
-   - Hessian matrix and regularization
-
-2. **[Part 1: Root Finding & Backward Euler](part1_root_finding.html)**
+1. **[Part 1a: Root Finding & Backward Euler](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)**
+   - Unconstrained optimization fundamentals
+   - Newton's method implementation
+   - Globalization strategies: Hessian matrix and regularization
+
 3. **[Part 2: Equality Constraints](part2_eq_constraints.html)**
    - Lagrange multiplier theory
    - KKT conditions for equality constraints
@@ -37,7 +38,6 @@ The class is structured around four interactive Jupyter notebooks:
 ## Additional Resources
 
 - **[Lecture Slides (PDF)](ISYE_8803___Lecture_2___Slides.pdf)** - Complete slide deck
-- **[Demo Script](penalty_barrier_demo.py)** - Python demonstration of penalty vs. barrier methods
 - **[LaTeX Source](main.tex)** - Source code for lecture slides
 
 ## Key Learning Outcomes
@@ -49,13 +49,6 @@ The class is structured around four interactive Jupyter notebooks:
 - Compare different constraint handling methods
 - Implement Sequential Quadratic Programming (SQP)
 
-## Prerequisites
-
-- Solid understanding of linear algebra and calculus
-- Familiarity with Julia programming
-- Basic knowledge of differential equations
-- Understanding of optimization concepts from Class 1
-
 ## Next Steps
 
 This class provides the foundation for advanced topics in subsequent classes, including Pontryagin's Maximum Principle, nonlinear trajectory optimization, and stochastic optimal control.

class02/overview.md

@@ -34,20 +34,21 @@ 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 1: Minimization via Newton's Method](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
 
-2. **[Part 1: Root Finding & Backward Euler](part1_root_finding.html)**
+1. **[Part 1a: Root Finding & Backward Euler](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)**
+   - 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)**
    - Lagrange multiplier theory
    - KKT conditions for equality constraints
@@ -98,10 +99,6 @@ The methods covered in this class are fundamental to:
 
 ## Further Reading
 
-- Nocedal, J., & Wright, S. J. (2006). *Numerical Optimization*
-- Boyd, S., & Vandenberghe, L. (2004). *Convex Optimization*
-- Betts, J. T. (2010). *Practical Methods for Optimal Control and Estimation Using Nonlinear Programming*
-
 ## Next Steps
 
 This class provides the foundation for the advanced topics covered in subsequent classes, including:
@@ -112,4 +109,4 @@ This class provides the foundation for the advanced topics covered in subsequent
 
 ---
 
-*For questions or clarifications, please refer to the interactive notebooks or contact the instructor.*
+*For questions or clarifications, please reach out to Arnaud Deza at [email protected]*