Aderivaldii/optimization-problems
🚀 A Python repository showcasing optimization techniques for Machine Learning including LP, Newton's methods, LASSO, and convex optimization. 📈🐍
What's novel
🚀 A Python repository showcasing optimization techniques for Machine Learning including LP, Newton's methods, LASSO, and convex optimization. 📈🐍
Code Analysis
20 files read · 5 roundsA collection of 10 modules implementing optimization algorithms from scratch (Newton's method, Levenberg-Marquardt, quasi-Newton SR1/DFP/BFGS, Augmented Lagrangian, Sequential Barrier Method, backtracking line search) and applying them to signal fitting, sparse representation, and constrained optimi
Strengths
Genuine algorithmic implementations with analytically computed gradients, Hessians, and Jacobians; the ALA+LM combination for constrained NLS and the from-scratch interior point method show real depth. Each module demonstrates a distinct optimization technique with proper convergence tracking and visualization.
Weaknesses
No tests whatsoever, significant code duplication (LM algorithm copy-pasted across 4+ files with minor model changes), no package structure or shared utilities, and the README overstates the project as a 'showcase' when it's clearly a student assignment collection. Error handling is minimal - no input validation, no convergence failure detection in most algorithms.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
13
Contributors
2
Files
32
Active weeks
2
Repository
Language
Python
Stars
1
Forks
0
License
—