Paolo Narváez

Bio: Paolo Narváez is an academic researcher from Bell Labs. The author has contributed to research in topics: Routing protocol & Dijkstra's algorithm. The author has an hindex of 1, co-authored 1 publications receiving 272 citations.

New dynamic algorithms for shortest path tree computation

Abstract: The open shortest path first (OSPF) and IS-IS routing protocols widely used in today's Internet compute a shortest path tree (SPT) from each router to other routers in a routing area Many existing commercial routers recompute an SPT from scratch following changes in the link states of the network Such recomputation of an entire SPT is inefficient and may consume a considerable amount of CPU time Moreover, as there may coexist multiple SPTs in a network with a set of given link states, recomputation from scratch causes frequent unnecessary changes in the topology of an existing SPT and may lead to routing instability We present new dynamic SPT algorithms that make use of the structure of the previously computed SPT Besides efficiency, our algorithm design objective is to achieve routing stability by making minimum changes to the topology of an existing SPT (while maintaining shortest path property) when some link states in the network have changed We establish an algorithmic framework that allows us to characterize a variety of dynamic SPT algorithms including dynamic versions of the well-known Dijkstra, Bellman-Ford, D'Esopo-Pape algorithms, and to establish proofs of correctness for these algorithms in a unified way The theoretical asymptotic complexity of our new dynamic algorithms matches the best known results in the literature

Receding Horizon Control

Abstract: In this article we have shown that receding horizon control offers a straightforward method for designing feedback controllers that deliver good performance while respecting complex constraints. A designer specifies the RHC controller by specifying the objective, constraints, prediction method, and horizon, each of which has a natural choice suggested directly by the application. In more traditional approaches, such as PID control, a designer tunes the controller coefficients, often using trial and error, to handle the objectives and constraints indirectly. In contrast, RHC con trollers can often obtain good performance with little tuning. In addition to the straightforward design process, we have seen that RHC controllers can be implemented in real time at kilohertz sampling rates. These speeds are useful for both real-time implementation of the controller as well as rapid Monte Carlo simulation for design and testing purposes. Thus, receding horizon control can no longer be considered a slow, computationally intensive policy. Indeed, RHC can be applied to a wide range of control problems, including applications involving fast dynamics.

Incremental nonlinear dimensionality reduction by manifold learning

Abstract: Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition, and machine learning. Several algorithms have been proposed to analyze the structure of high-dimensional data based on the notion of manifold learning. These algorithms have been used to extract the intrinsic characteristics of different types of high-dimensional data by performing nonlinear dimensionality reduction. Most of these algorithms operate in a "batch" mode and cannot be efficiently applied when data are collected sequentially. In this paper, we describe an incremental version of ISOMAP, one of the key manifold learning algorithms. Our experiments on synthetic data as well as real world images demonstrate that our modified algorithm can maintain an accurate low-dimensional representation of the data in an efficient manner.

Method and apparatus for focused crawling

Abstract: The present invention pertains to the field of computer software. More specifically, the present invention relates to dynamic discovery of documents or information through a focused crawler or search engine.

Finding time-dependent shortest paths over large graphs

Abstract: The spatial and temporal databases have been studied widely and intensively over years. In this paper, we study how to answer queries of finding the best departure time that minimizes the total travel time from a place to another, over a road network, where the traffic conditions dynamically change from time to time. We study a generalized form of this problem, called the time-dependent shortest-path problem. A time-dependent graph GT is a graph that has an edge-delay function, wi, j(t), associated with each edge (vi, vj), to be stored in a database. The edge-delay function wi, j(t) specifies how much time it takes to travel from node vi to node vj, if it departs from vi at time t. A user-specified query is to ask the minimum-travel-time path, from a source node, vs, to a destination node, ve, over the time-dependent graph, GT, with the best departure time to be selected from a time interval T. We denote this user query as LTT(vs, ve, T) over GT. The challenge of this problem is the added complexity due to the time dependency in the time-dependent graph. That is, edge delays are not constants, and can vary from time to time. In this paper, we propose a novel algorithm to find the minimum-travel-time path with the best departure time for a LTT(vs, ve, T) query over a large graph GT. Our approach outperforms existing algorithms in terms of both time complexity in theory and efficiency in practice. We will discuss the design of our algorithm, together with its correctness and complexity. We conducted extensive experimental studies over large graphs and will report our findings.