'Network'is a heavily overloaded term, so that 'network analysis' means different things to
different people. Specific forms of network analysis are used in the study of diverse …

We analyze the semidefinite programming (SDP) based model and method for the position
estimation problem in sensor network localization and other Euclidean distance geometry …

Recently, a semidefinite programming (SDP) relaxation approach has been proposed to
solve the sensor network localization problem. Although it achieves high accuracy in …

In many applications of wireless ad hoc and sensor networks, position-awareness is of great
importance. Often, as in the case of geometric routing, it is sufficient to have virtual …

Classification is the key to understand large and complex systems that are made up of many
individual parts. For example in the study of food webs (networks that consist of living …

Approximation Algorithms for Low-Distortion Embeddings Into Low-Dimensional Spaces Page
1 Approximation Algorithms for Low-Distortion Embeddings Into Low-Dimensional Spaces …

A low-distortion embedding between two metric spaces is a mapping which preserves the
distances between each pair of points, up to a small factor called distortion. Low-distortion …

We introduce a new notion of embedding, called minimum-relaxation ordinal embedding,
parallel to the standard notion of minimum-distortion (metric) embedding. In an ordinal …

Recently, a semidefinite programming (SDP) relaxation approach has been proposed to
solve the sensor network localization problem. Although it achieves high accuracy in …

Dimensionality reduction plays a central role in real-world applications for Machine
Learning, among many fields. In particular, metric dimensionality reduction where data from …