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6FMAI19 Lectures

What we plan to cover:

  • Gradient descent
  • Proximal point algorithm
  • Constrained optimization
  • Accelerated methods
  • Subgradient methods
  • Stochastic gradient method
  • Mirror descent
  • Primal-dual methods
  • Variational inequalities
  • Second-order methods
  • Various applications
Date Lecture Scribe Homework
19.01.22 Lecture 1. Introduction --- ---
26.01.22 Lecture 2. Convexity. Optimality condition lecture-2.pdf  
2.02.22 Lecture 3. L-smooth functions. Gradient descent lecture-3.pdf assignment-1.pdf
9.02.22 Lecture 4. GD for convex functions. Constrained Optimization lecture-4.pdf  
16.02.22 Lecture 5. Projected Gradient Method. Subgradient lecture-5.pdf  
23.02.22 Lecture 6. Projected Subgradient Method lecture-6.pdf  
2.03.22 Lecture 7. Empirical Risk Minimization Problem. Stochastic Subgradient Method  
9.02.22 Lecture 8. Stochastic Gradient Method lecture-8.pdf assignment-2.pdf
23.03.22 Lecture 9. Exercises + Subgradient method for strongly convex case lecture-9.pdf  
30.03.22 Lecture 10. Proximal point algorithm lecture-10.pdf  
6.04.22 Lecture 11. Duality
13.04.22 Lecture 12. Smoothness and strong convexity lecture-12.pdf  
20.04.22 Lecture 13. Using duality in practice lecture-13.pdf  

Template for scribing lectures: template.tex


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Senast uppdaterad: 2022-04-29