Given a set of basic binary features, we propose a new L1 norm SVM based feature selection method that explicitly selects the features in their polynomial or tree kernel spaces. The efficiency comes from the anti-monotone property of the subgradients: the subgradient with respect to a combined feature can be bounded by the subgradient with respect to each of its component features, and a featur...

In this paper, we consider a generic inexact subgradient algorithm to solve a nondifferentiable quasi-convex constrained optimization problem. The inexactness stems from computation errors and noise, which come from practical considerations and applications. Assuming that the computational errors and noise are deterministic and bounded, we study the effect of the inexactness on the subgradient ...

The supply chain optimization of continuous process networks is essential for most chemical companies. The dynamic nature of this problem leads to systems that involve types of chemicals as well as multiple time periods, and ultimately many complex and large combinatorial optimization models. These models become very difficult to solve, and sometimes not even solvable. Hence, such models requir...

In this paper we propose a purely distributed dynamic network routing algorithm that simultaneously regulates queue sizes across the network. The algorithm is distributed since each node decides on its outgoing link flows based only on its own and its immediate neighbors’ information. Therefore, this routing method will be adaptive and robust to changes in the network topology, such as the node...

We present the numerical behavior of a projection method for convex minimization problems which was studied by Cegielski [1]. The method is a modification of the Polyak subgradient projection method [6] and of variable target value subgradient method of Kim, Ahn and Cho [2]. In each iteration of the method an obtuse cone is constructed. The obtuse cone is generated by a linearly independent sys...

In this paper, we consider the problem of minimizing the sum of nondifferentiable, convex functions over a closed convex set in a real Hilbert space, which is simple in the sense that the projection onto it can be easily calculated. We present a parallel subgradient method for solving it and the two convergence analyses of the method. One analysis shows that the parallel method with a small con...

In a real Hilbert space, we aim to investigate two modified Mann subgradient-like methods find solution pseudo-monotone variational inequalities, which is also common fixed point of finite family nonexpansive mappings and an asymptotically mapping. We obtain strong convergence results for the sequences constructed by these proposed rules. give some examples illustrate our analysis.

In this paper, we attempt to solve the problem of min-cost multicast routing for multi-layered multimedia distribution. More specifically, for (i) a given network topology (ii) the destinations of a multicast group and (iii) the bandwidth requirement of each destination, we attempt to find a feasible routing solution to minimize the cost of a multicast tree for multi-layered multimedia distribu...

SubGradient based Blind Algorithm (SGBA) has recently been introduced [A.T. Erdogan, C. Kizilkale, Fast and low complexity blind equalization via subgradient projections, IEEE Trans. Signal Process. 53 (2005) 2513–2524; C. Kizilkale, A.T. Erdogan, A fast blind equalization method based on subgradient projections, Proceedings of IEEE ICASSP 2004, Montreal, Canada, vol. 4, pp. 873–876.] as a conv...