1,721,655 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Modelling Robust Design Problems via Conic Optimization
No full textThis thesis deals with optimization problems with uncertain data. Uncertainty here means that the data is not known exactly at the time when its solution has to be determined. In many models the uncertainty is ignored and a representative nominal value of the data is used. The uncertainty may be due to measurement or modelling errors or simply to the unavailability of the information at the time of the decision. We use conic optimization (CO) models to find robust optimal solution of some uncertain design problems. We demonstrate this for the robust shortest path problem (RSPP), the robust maximum flow problem (RMFP) and the robust resistance network topology design (RNTD) problem. Robust optimal design of these problems are obtained by using the robust counterpart (RC) methodology of Ben-Tal and Nemirovskii. In the RSPP and RMFP, the uncertainty occurs only in the objective vector. We consider two types of uncertainty sets, namely boxes and ellipsoidal sets. The robust counterpart of RSPP with ellipsoidal uncertainty is a conic quadratic problem (CQP) with binary variables. Therefore it is not a computationally tractable problem since we need a branch and bound scheme to solve the problem. It needs further investigation. On the other hand, for both uncertainty types of sets, the RMFP is a usual maximum flow problem with modified arc capacities, and hence the RMFP can be solved in polynomial time. As far as we know this result is new. We present also a parametric variant for the RSPP and RMFP with ellipsoidal uncertainty set. In the RNTD problem, the robust model is obtained by using a simple variational principle which enables us to obtain the semidefinite representation of the dissipation. This implies that the multi-current case and a robust version of RNTD problem can be represented as a semidefinite problem. Using this semidefinite model, the robustness of a resistance network can be significantly improved.Electrical Engineering, Mathematics and Computer Scienc
Jordan algebraic approach to symmetric optimization
No full textIn this thesis we present a generalization of interior-point methods for linear optimization based on kernel functions to symmetric optimization. It covers the three standard cases of conic optimization: linear optimization, second-order cone optimization and semi-definite optimization. We give an introduction to Euclidean Jordan algebras and explain the connection between such algebras and symmetric cones. We establish some properties of eigenvalues in Jordan algebras and prove that the barrier functions based on kernel functions are separable spectral functions that only depend on the eigenvalues of their arguments. We propose an interior-point algorithm for symmetric optimization and derive its complexity bound.Electrical Engineering, Mathematics and Computer Scienc
Parallel implementation of interior-point methods for semidefinite optimization
No full textElectrical Engineering, Mathematics and Computer Scienc
Full-step interior-point methods for symmetric optimization
No full textIn [SIAM J. Optim., 16(4):1110--1136 (electronic), 2006] Roos proposed a full-Newton step Infeasible Interior-Point Method (IIPM) for Linear Optimization (LO). It is a primal-dual homotopy method; it differs from the classical IIPMs in that it uses only full steps. This means that no line searches are needed. In this thesis, we first present an improved full-Newton step IIPM for LO. Then, based on the properties of Euclidean Jordan algebras, we generalize the improved full-Newton step IIPM for LO to full Nesterov-Todd step (NT-step) IIPM for Symmetric Optimization (SO). Since the analysis requires a quadratic convergence result for the feasible case, primal-dual feasible IPMs with full steps are presented as well. Although our devised IIPMs admit the best known iteration bound, from a practical perspective they are not efficient. This is because they always perform according to their worst-case theoretical complexity bounds, which means that only tiny reductions of the so-called barrier parameter are admitted. As a remedy, we propose a more aggressive (adaptive) updating strategy. Finally, our full NT-step IIPM for SO is implemented with both standard and adaptive updates of the barrier parameter. The significant improvement in performance of the adaptive updating strategy over the original short updating strategy is illustrated. The algorithm with adaptive updates is also used to solve problems from the well known library SDPLIB [Optim. Methods Softw., 11/12(1-4):683--690, 1999] of test problems. The results are promising, and to some extend competing with SDPT3 [Math. Program., 95(2, Ser. B):189--217, 2003].Software TechnologyElectrical Engineering, Mathematics and Computer Scienc
Full-Newton step interior-point methods for conic optimization
No full textIn the theory of polynomial-time interior-point methods (IPMs) two important classes of methods are distinguished: small-update and large-update methods, respectively. Small-update IPMs have the best theoretical iteration bound and IPMs with full-Newton steps belong to this class of methods. Within each of these classes one has feasible and infeasible interior-point methods (IIPMs). In this thesis we first deal with full-Newton step IIPMs, and we consider feasible full-Newton step IPMs.Electrical Engineering, Mathematics and Computer Scienc
On the Central Path of Redundant Klee-Minty Problems
No full textSoftware TechnologyElectrical Engineering, Mathematics and Computer Scienc
Large-Update Infeasible Interior-Point Methods for Linear Optimization
No full textRecently, C. Roos proposed a full-Newton step infeasible interior-point method (IIPM) for linear optimization (LO). Shortly afterwards, Mansouri and Roos presented a variant of this algorithm and Gu et al. a version with a simplified analysis. Roos' algorithm is a path-following method. It uses the so-called homotopy path as a guideline to an optimal solution. The algorithm has the advantage that it uses only full Newton steps (the step size is always 1, hence requires no computation), and its convergence rate is O(n), which coincides with the best known convergence rate for IIPMs. Apart from these nice features, the algorithm has the deficiency that it is a small-update method and hence it is too slow for practical purposes. In this thesis we design a large-update version of Roos' algorithm. We present a practically efficient implementation of (a variant of) the algorithm and compare its performance with that of the well- known LIPSOL package. The numerical results are promising as the iteration numbers of our algorithm are close to those of LIPSOL; in a few cases they outperform LIPSOL. Not surprisingly, as in large-update feasible interior-point methods (FIPMs), there is a gap between the practical and the theoretical behavior of our large-update IIPM. To be more precise, its theoretical convergence rate is O(n?n (log n)³) which is worse than the convergence rate of its full-Newton step variant. This phenomenon is well-known in the field of IPMs, and has been called the irony of IPMs: small-update methods have the best complexity results and are slow in practice, whereas large-update methods have worse complexity results and excellent performance in practice. For example, large-update FIPMs are by a factor worse than that of the full-Newton step FIPMs, i.e., O(?nlogn) versus O(?n). The thesis also contains a survey of IIPMs that have been presented by several authors in last two decades. It covers a wide range of methods, starting from Lustig's algorithm, to the infeasible potential-reduction methods of Mizuno, Kojima and Todd. We focus on convergence properties and polynomiality of the IIPMs presented in our survey.EWIElectrical Engineering, Mathematics and Computer Scienc
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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