A Randomized Cutting Plane Method with Probabilistic Geometric Convergence

A Homogenized Cutting Plane Method

Analytic Center Cutting-Plane Method

An Improved Cutting Plane method for the solution of Probabilistic Constrained Problem with Discrete Random Variables

We consider a probabilistic constrained stochastic programming problem with discrete random variables. Two methods, a cutting plane and a column generation method have already been developed for the solution of the problem. In this paper we blend them together and obtain a method that is faster than the earlier ones. We also present a refined algorithm for the generation of p-level efficient po...

Learning Data Manifolds with a Cutting Plane Method

We consider the problem of classifying data manifolds where each manifold represents invariances that are parameterized by continuous degrees of freedom. Conventional data augmentation methods rely upon sampling large numbers of training examples from these manifolds; instead, we propose an iterative algorithm called MCP based upon a cutting-plane approach that efficiently solves a quadratic se...

A probabilistic analytic center cutting plane method for feasibility of uncertain LMIs

Many robust control problems can be formulated in abstract form as convex feasibility programs, where one seeks a solution x that satisfies a set of inequalities of the form F . = {f (x, ) 0, ∈ D}. This set typically contains an infinite and uncountable number of inequalities, and it has been proved that the related robust feasibility problem is numerically hard to solve in general. In this pap...