ISE789/OR791 Optimization Models for Systems Analytics

ISE 789/OR 791: Optimization Models for Systems Analytics


Syllabus References

Lecture Notes

Lecture 0    |   Lecture 1    |   Lecture 2     |   Lecture 3   |   Lecture 4    |   Lecture 5    |   Lecture 6 

Supplemental Reading Material

  • The Use of Binary Choice Forests to Model and Estimate Discrete Choices
  • Sparse Signal Reconstruction via the Approximations of L0 Quasinorm
  • Sparse Solutions by a Quadratically Constrained Lq (0<q<1) Minimization Model
  • A Gradient Descent Based Algorithm for Lp Minimization
  • A Unified Smooth Framework For Nonconvex Penalized Least Squares Problems
  • Optimization for Deep Learning: an Overview
  • Simple and Fast Iterative Algorithm for (Binary Integer) Online Linear Programming
  • How Can Machine Learning and Optimization Help Each Other Better?
  • Deep Learning in Business Analytics and Operations Research: Models, Applications and Managerial Implications
  • Customer Choice Models versus Machine Learning: Finding Optimal Product Displays on Alibaba
  • Simple and Fast Algorithm for Binary Integer and Online Linear Programming
  • A Sub-one Quasi-norm-based Similarity Measure for Collaborative Filtering in Recommender Systems
  • DC Programming: The Optimization Method You Never Knew You Had to Know
  • Sparse Optimization Lecture: Sparse Recovery Guarantees
  • Convex Analysis and Duality over Discrete Domains
  • Using Radial Basis Function Networks for Function Approximation and Classification
  • Relaxed conditions for radial-basis function networks to be universal approximators
  • The Turbulent Past and Uncertain Future of AI
  • Covariance matrix
  • k-means clustering
  • Quadratically Constrained Quadratic Programming
  • Linear Conic Programming Theory and Applications Lecture 1 | Lecture 2 | Lecture 3 | Lecture 4 | Lecture 5


Assignment #1  Due:  

Assignment #2   Due:  

Assignment #3   Due:  

Assignment #4   Due:  

Assignment #5   Due:  


Project Description

  • Proposal Due:    
  • Report Due:    


Final Exam: