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Code written for the 3. task of Model Analysis 1 in the year 2023/24.
49 commits 1 branch 0 tags 43 MiB
  • Python 76.1%
  • TeX 23.9%
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Marko Urbanc 4cad7d89b1 fix: 🚑 small addition of critical detail
2023-10-26 10:22:23 +02:00
Code feat: 🚀 .pdf upload 2023-10-26 10:19:05 +02:00
Images feat: 🎨 add thomson multiplot 2023-10-26 10:02:55 +02:00
Latex fix: 🚑 small addition of critical detail 2023-10-26 10:22:23 +02:00
Results feat: 🐛 fix some boundary issues 2023-10-26 01:53:30 +02:00
Videos feat: 🎨 create animation of thomson problem 2023-10-23 01:42:19 +02:00
.gitignore feat: ✨ start work on thomson performance plot 2023-10-25 12:03:12 +02:00
charges_10.gif feat: 🎨 create animation of thomson problem 2023-10-23 01:42:19 +02:00
MarkoUrbanč_103.pdf fix: 🚑 small addition of critical detail 2023-10-26 10:22:23 +02:00
mod103_instructions.pdf init: 🎉 initial commit 2023-10-20 01:04:04 +02:00
README.md docs: 📝 add README 2023-10-20 19:40:01 +02:00

README.md

Nonlinear minimization

Things I'd like to do

  • Implement a nonlinear minimization algorithm

  • Create a suite of test functions

  • Regression via Bayesian inference

  • Regression via Boosted Decision Trees

  • Regression via Neural Networks

  • Optimize via Gurobi, a commercial optimization solver

  • Optimize via SCIP, an open-source optimization solver

  • Optimize via scipy.optimize.minimize()

    • Nelder-Mead
    • Powell
    • CG (conjugate gradient)

  • Compare performance of different algorithms

  • Gurobi nodes vs. runtime plot

  • Massively parallel grid search