Linear programming in infinite-dimensional spaces: E.J. Anderson and P. Nash Wiley, New York, 1987, xi + 172 pages

TL;DR: This paper treats numerical methods based on either discretization or local reduction with the emphasis on the design of superlinearly convergent (SQP-type) methods.

TL;DR: It is argued that many known interior point methods for linear programs can be transformed in a mechanical way to algorithms for SDP with proofs of convergence and polynomial time complexity carrying over in a similar fashion.

TL;DR: In this article, the stochastic 2D Navier-Stokes equations on the torus driven by degenerate noise are studied and the smallest closed invariant subspace for this model and the dynamics restricted to that subspace is shown to be ergodic.

TL;DR: In this article, the authors show that stable matching with quasi-linear preferences is equivalent to stable pricing with transferable utilities and a participation constraint, and to an optimal transportation (Monge-Kantorovich) linear programming problem.