In this article we study support vector machine (SVM) classifiers in the face of uncertain knowledge sets and show how data uncertainty in knowledge sets can be treated in SVM classification by employing robust optimization. We present knowledge-based SVM classifiers with uncertain knowledge sets using convex quadratic optimization duality. We show that the knowledge-based SVM, where prior know...

Designing a supply chain to comply with environmental policy requires awareness of how work and/or production methods impact the environment and what needs be done reduce those impacts make company more sustainable. This is dynamic process that occurs at both strategic operational levels. However, being environmentally friendly does not necessarily mean improving efficiency system same time. Th...

August 24, 2015 Abstract Every quadratic optimization problem with a mix of continuous and binary variables can be equivalently reformulated as a completely positive optimization problem, i.e., a linear optimization problem over the convex but computationally intractable cone of completely positive matrices. In this paper, we focus on general inner approximations of the cone of completely posit...

We are considering the application of the Augmented Lagrangian algorithms with quadratic penalty, to convex problems of quadratic programming. The problems of quadratic programming are composites of quadratic objective function and linear constraints. This important class of problems will be generated through the algorithm of sequential quadratic programming, where at each iteration the quadrat...

Quadratic hedging of option payoffs generates the variance optimal martingale measure. When an features exercise policy and its cash flows are hedged according to this approach, it may be tempting optimize such a under Because measure not equivalent probability measure, focusing on American options we show that resulting unappealing. This drawback can sometimes remedied by imposing time consist...