EN813 711 Optimization: Hard, Soft, and Emerging Approaches

Semester 1/2567 (Jun-Oct 2024)

Course description

Overview of optimization. Review of related background. Introduction to computational science. Application Examples. One-dimensional numerical optimization. Multi-dimensional optimization. Constrained optimization. Combinatorial optimization. Search. Emerging approach.

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Instructor

• Tatpong Katanyukul

Textbooks

• Novak, Numerical Methods for Scientific Computing, Equal Share Press 2022
• Chong and Zak, Introduction to Optimization, Wiley 2013
• Russell and Norvig, Artificial Intelligence: Modern Approach, Pearson 2021
• Yanofsky and Mannucci, Quantum Computing for Computer Scientists, Cambridge 2008

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Assessments (tentative)

• Participation: 10%
• Assignments/Projects/Final Evaluation: 50%
• Exercises/Homeworks: 40%
    • autolab.en.kku.ac.th

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New tool

https://mozart.en.kku.ac.th:8443

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Main materials

• Orientation and Overview
• Numerical Computation and Visualization.
    • Hands-on: Numpy (Mozart)
    • Hands-on: Matplotlib (Mozart)
• Introduction to Computational Science and Engineering
    • Hands-on: CSE 1 with Numpy.linalg (Mozart)
    • Hands-on: CSE 2 with Scipy (Mozart)
      • Alignment problem data
    • Hands-on: CSE 3 (Mozart) Future addition!
• Introduction to Artificial Intelligence
• Optimization
      • Hands-on: OPT1 Conditions (Mozart)
      • Hands-on: OPT2 1D search algorithms (Mozart)
      • Hands-on: OPT3 Unconstrained optimization algorithms (Mozart)
      • Hands-on: OPT4 Constrained optimization (Mozart)
• Search
      • Hands-on: SRH1 uninformed search (Mozart)
• Heuristic search
      • Hands-on: SRH2 heuristic search with aux (Mozart)
• Local search
      • Hands-on: SRH3 local search with aux (Mozart)
      • Hands-on: combinatorial optimization: TSP with aux1 and aux2 (Mozart)
      • Hands-on: combinatorial optimization: bin packing with aux1 and aux2 (Mozart) Future?
      • Hands-on: combinatorial optimization: max cut with aux1 and aux2 (Mozart) Future?
• Emerging Approach 2024
    • Quantum computing in 180 minutes (Mozart)
    • Quantum programming (Sim, QC) (Sim: Colab, QC: Colab)
    • Quantum gates: experiments (Colab)
    • Quantum search (Colab)
    • Schrödinger equation (Mozart)
  
• Intellectual Exploration

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Related materials

• Dynamic programming (algorithmic paradigm)
• Dynamic programming (solving Bellman equation) Future?
• OPT 2024
• Miscellaneous
•   • COE Wiki

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Academic Honesty

You are expected to do your own work to show understanding, skills, and what you have learned. All submitted works (including HOMEWORKS!) should be your own and ACADEMIC DISHONESTY IS NOT ALLOWED.
Academic dishonesty includes:

• Copying words, ideas, codes, or other materials from another source without giving credit to the original author;
• Copying from your peers or seniors, and then submitting the work as your own;
• Employing or letting another person to alter, revise, or edit your work, and then submitting the work as your own;
• Intentionally letting any of your peers to copy your work and submit as one's own;
• Submitting work automatically produced by an emerging tool (e.g., AI) as your work.

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Last updated 2024 July 29th.