Monotone Processes

Instructor: Tianyu Zhang

Contact: bidenbaka@gmail.com

Website: tymathdb.com

Level: Graduate

Language: English

Time: Winter 2022, December.

Location: Online

Discussion Time: TBD

Prerequisite:

Functional Analysis, Measure Theory, Real Analysis, Convex Analysis.

Abstract:

Analogues of many of the structures in real linear algebra have been studied under the heading of “convexity”. For example, the notions of subspace, affine sets(known as affine manifolds), system of linear equations, linear functionals, correspond respectively to the more general notions of convex cone, convex set, system of linear(or convex or concave) inequalities, positively homogeneous convex(or concave) functionals.

Strangely missing from this list is anything corresponding to “linear transformation”. Certain “convex transformations”, to be sure, have arisen naturally out of economic theory.

The concept of “monotone processes”, to which this seminar is devoted, does fill the gap corresponding to “linear transformation”, at least in a small way. It allows us to study convex analogues of all the topics mentioned above.

Generally speaking, monotone processes generalize not “linear transformation” but “positive linear transformation”. To the last part of this paper, in particular, is best viewed as an extension of the Perron-Frobenius theory of positive matrices rather than of ordinary eigenvalue theory. There is, however, a more general possible concept of “convex process”(see this in the last parts of the Convex Analysis) which does not have these limitations of positivity and monoticity. 

Reference: R. Tyrrell Rockafellar, Monotone Processes of Convex and Concave Type, Memories of the American Mathematical Society, Number 77.

Syllabus:

Lecture 1: Definition of Monotone Processes

  • File
  • Video
    • not yet available

Lecture 2: Adjoints and Kuhn-Tucker Functions

Lecture 3: Inverse and Polars

Lecture 4: Monotone Convex Programs

Lecture 5: Combinatorial Operations

Lecture 6: Sub-eigenvalues and Growth Rates

Lecture 7: Eigensets

Lecture 8: Behavior in the Limit