LLM-OPT/_toc.yml at main · OptimAI-Lab/LLM-OPT · GitHub

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format: jb-book
root: index
options:
  numbered: true      # <-- THIS makes the sidebar show 1, 1.1, 1.2, ...
  maxdepth: 3         # how deep the sidebar expands (H1–H3)
chapters:
  - file: lectures/Chapter1/course_intro
    title: Introduction to the Course
    sections:
      - file: lectures/Chapter1/intro_to_optimization
        title: Introduction to Optimization
      - file: lectures/Chapter1/notations
        title: Notations
  - file: lectures/Chapter2/optimization
    title: Classical Optimization Theory
    sections:
      - file: lectures/Chapter2/Unconstrained_Optimization
        title: Unconstrained Optimization
      - file: lectures/Chapter2/gradient
        title: Gradient Methods
      - file: lectures/Chapter2/Constrained_Optimization
        title: Constrained Optimization
      - file: lectures/Chapter2/Examples
        title: Extension and Application of Gradient Descent Based Algorithms
      - file: lectures/Chapter2/Duality
        title: Duality, Lagrangian Multiplier Theorem, and KKT Conditions
  - file: lectures/Chapter3/LLM
    title: Introduction to Large Language Models
  - file: lectures/Chapter4/stochastic_opt
    title: Stochastic Optimization for Large Language Models
    sections:
      - file: lectures/Chapter4/sgd
        title: Stochastic Gradient Descent
      - file: lectures/Chapter4/training_algorithms
        title: Adaptive algorithms
      - file: lectures/Chapter4/variants
        title: Discussions on modern adaptive methods
      - file: lectures/Chapter4/lr_mup
        title: Practical concerns on training stability
      - file: lectures/Chapter4/zeroth_order
        title: Zeroth-order optimization for LLM
  - file: references.bib
    title: References