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- Sanjeeb Dash, Joao Goncalves, Jay Kalagnanam, Ali Koc, Ming Zhao, J. Goez
- 2010

In this paper we investigate families of quadrics that have fixed intersections with two given hyperplanes. The cases when the two hyperplanes are parallel and when they are nonparallel are discussed. We show that these families can be described with only one parameter. In particular we show how the quadrics are transformed as the parameter changes. This… (More)

We study the convex hull of the intersection of a convex set E and a disjunctive set. This intersection is at the core of solution techniques for Mixed Integer Convex Optimization. We prove that if there exists a cone K (resp., a cylinder C) that has the same intersection with the boundary of the disjunction as E, then the convex hull is the intersection of… (More)

We investigate the derivation of disjunctive conic cuts for mixed integer second order cone optimization (MISOCO). These conic cuts characterize the convex hull of the intersection of a disjunctive set and the feasible set of a MISOCO problem. We present a full characterization of these inequalities when the disjunctive set considered is defined by parallel… (More)

Markov chains (MC) are a powerful tool for modeling complex stochastic systems. Whereas a number of tools exist for <i>solving</i> different types of MC models, the first step in MC modeling is to define the model parameters. This step is, however, error prone and far from trivial when modeling complex systems. In this article, we introduce jMarkov, a… (More)

We study the convex hull of the intersection of a disjunctive set defined by parallel hy-perplanes and the feasible set of a mixed integer second order cone optimization (MISOCO) problem. We extend our prior work on disjunctive conic cuts (DCCs), which has thus far been restricted to the case in which the intersection of the hyperplanes and the feasible set… (More)

When analyzing real life stochastic systems in most cases is easier, cheaper and more effective to use analytical models rather than studying the physical system or a simulation model of it. The stochastic modeling is a powerful tool that helps the analysis and optimization of stochastic systems.
However the use of stochastic modeling is not widely spread… (More)

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