MA630 Advanced Optimization Methods

Course Catalog Description


The objective of this course is to introduce the students to the some advanced topics in the theory and methods of optimization. The first portion of the class focuses on subgradient calculus for non-smooth convex functions, some convex analysis results. optimality conditions for non-smooth optimization problems, composite optimization problems, conjugate and Lagrangian duality for non-smooth problems. Problems defined by differentiable but possibly non-convex functions will be reviewed briefly. The second part of the class discusses numerical methods for non-smooth optimization as well as approaches to large-scale optimization problems. The latter include decomposition methods, design of distributed and parallel methods of optimization, as well as stochastic approximation methods. Along with the theoretical results and methods, examples of optimization models in statistical learning and data mining, compressed sensing and image reconstruction will be discussed in order to illustrate the challenges and the phenomena, and to demonstrate the scope of applications.

Prerequisites: The students are expected to have knowledge of calculus, linear algebra, and optimization corresponding to Ma 230 or an equivalent course.

Campus Fall Spring Summer
On Campus
Web Campus


Professor Email Office
Darinka Dentcheva

More Information

Course Outcomes

Students who successfully complete this course will be able to

Learning Goals:

  1. The students will be able to use the following notions: subgradients of convex and Lipschitz functions; conjugate and bi-conjugate functions, and stochastic subgradients both for the purpose of theoretical analysis as well as in numerical methods.
  2. The students would be familiar with the use of Fenchel and Lagrange duality and will be able to analyze problems and devise numerical methods using duality relations.
  3. The students would be able to use subgradient calculus and calculate subgradients of conjugate and of dual functions applying Fenchel-Moreau theorem, Rockafellar- Moreau theorem and other results.
  4. The students will be able to use first order necessary and sufficient conditions of optimality for problems with non-differentiable functions and for composite problems (composition of non-convex-smooth with convex-non-smooth functions), to verify various constraint qualification conditions.
  5. The students will be familiar with the basic steps of the numerical methods listed in the syllabus, their proof of convergence, advantages and disadvantages.
  6. The students will be able to apply the fundamental decomposition and distribution approaches to create tailored distributed methods for particular applied problems. They will be aware of the applicability and convergence properties of the methods.
  7. The students will understand and use stochastic approximation and randomized techniques. They will be able to identify when these techniques are needed and how to apply them.

Course Resources


Andrzej Ruszczynski, Nonlinear Optimization, Princeton University Press, 2006.
Lecture Notes distributed in class.

Additional References

Supplementary Text(Not required)
  • James C. Spall, Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Wiley, Hoboken, 2003.
  • Heinz H. Bauschke, Patrick L. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, Springer, 2011.


Grading Policies

Seven homework assignments will be given. In addition, 4 short in-class examinations (quizzes) and a final exam will be conducted. The final grade will be based on the following score: 0.35×Assignments+0.35×In-class Quizzes+0.3×Final Project The syllabus is subject to change. All announcements about this class will be posted on Canvas. All assignments have to be submitted by their due date. Late submissions will be accepted only if the student has obtained a written permission. Submissions, which are late beyond one week, will not be accepted.

Lecture Outline

Week 1 Review of basic properties of convex sets.
Week 2 Non-smooth functions. Subgradient calculus.
Week 3 Tangent and normal cones.
Week 4 Optimality conditions
Week 5 Optimality conditions
Week 6 Fenchel and Lagrange duality
Week 7 Gradient descent and Newton's method
Week 8 Conjugate gradient method and quasi-Newton's methods
Week 9 Subgradient and non-gradient methods of optimization.
Week 10 Cutting plane methods. Proximal point method.
Week 11 Bundle methods. Trust region methods.
Week 12 Dual decomposition approaches: ADMM, ASM, ADAL.
Week 13 Stochastic subgradient methods.
Week 14 Stochastic methods with constraints.