Updating constraint preconditioners for KKT systems in quadratic programming via low-rank corrections

This work focuses on the iterative solution of sequences of KKT linear systems arising in interior point methods applied to large convex quadratic programming problems. This task is the computational core of the interior point procedure and an efficient preconditioning strategy is crucial for the efficiency of the overall method. Constraint preconditioners are very effective in this context; nevertheless, their computation may be very expensive for large-scale problems, and resorting to approximations of them may be convenient. Here we propose a procedure for building inexact constraint preconditioners by updating a "seed" constraint preconditioner computed for a KKT matrix at a previous interior point iteration. These updates are obtained through low-rank corrections of the Schur complement of the (1,1) block of the seed preconditioner. The updated preconditioners are analyzed both theoretically and computationally. The results obtained show that our updating procedure, coupled with an adaptive strategy for determining whether to reinitialize or update the preconditioner, can enhance the performance of interior point methods on large problems.Comment: 22 page

Directional TGV-based image restoration under Poisson noise

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identifica-tion of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach

Preparazione e applicazioni sintetiche di monoacetali di succinaldeidi

Synthetic methods for the preparation of substituted succinaldehyde monoacetals are reviewed and the use of these compounds as intermediates in several synthetic processes is reported

Primary gas thermometry by means of laser-absorption spectroscopy: Determination of the Boltzmann constant

We report on a new optical implementation of primary gas thermometry based on laser absorption spectrometry in the near infrared. The method consists in retrieving the Doppler broadening from highly accurate observations of the line shape of the R(12) $\nu_{1} + 2 \nu_{2}^{\phantom{1}0} + \nu_{3}$ transition in CO$_{2}$ gas at thermodynamic equilibrium. Doppler width measurements as a function of gas temperature, ranging between the triple point of water and the gallium melting point, allowed for a spectroscopic determination of the Boltzmann constant with a relative accuracy of $\sim1.6\times10^{-4}$.Comment: Submitted to Physical Review Letter

Sistema integrado de navegación y sensado en campo para relevamiento de parámetros biofísicos en diferentes estados fenológicos de maíz

La recopilación de datos fenotípicos de un cultivo a campo comprende tareas como el relevamiento y monitoreo sistemático, el análisis y la evaluación de los datos relevados (RMAE). Este trabajo presenta un modelo RMAE basado en un sistema de navegación integrado con sensado de imágenes en campo. Se utilizan técnicas de visión artificial y procesamiento de señales no sólo para la recolección de los datos biofísicos de cultivo de maíz a lo largo de la evolución de sus estados fenológicos, sino también para el monitoreo y evaluación posterior de los mismos. La presente solución plantea el uso de una plataforma de navegación por ensayos integrada con la interacción de dos módulos de trabajo independientes. El objetivo es automatizar el estudio de aspectos cualitativos y cuantitativos del cultivo de maíz, en relación a su variabilidad fenológica, morfológica, fisiológica, temporal y espacial

Patient well‐being after general anaesthesia: a prospective, randomized, controlled multi‐centre trial comparing intravenous and inhalation anaesthesia

Background. The aim of this study was to assess postoperative patient well‐being after total i.v. anaesthesia compared with inhalation anaesthesia by means of validated psychometric tests. Methods. With ethics committee approval, 305 patients undergoing minor elective gynaecologic or orthopaedic interventions were assigned randomly to total i.v. anaesthesia using propofol or inhalation anaesthesia using sevoflurane. The primary outcome measurement was the actual mental state 90 min and 24 h after anaesthesia assessed by a blinded observer using the Adjective Mood Scale (AMS) and the State‐Trait‐Anxiety Inventory (STAI). Incidence of postoperative nausea and vomiting (PONV) and postoperative pain level were determined by Visual Analogue Scale (VAS) 90 min and 24 h after anaesthesia (secondary outcome measurements). Patient satisfaction was evaluated using a VAS 24 h after anaesthesia. Results. The AMS and STAI scores were significantly better 90 min after total i.v. anaesthesia compared with inhalation anaesthesia (P=0.02, P=0.05, respectively), but equal 24 h after both anaesthetic techniques (P=0.90, P=0.78, respectively); patient satisfaction was comparable (P=0.26). Postoperative pain was comparable in both groups 90 min and 24 h after anaesthesia (P=0.11, P=0.12, respectively). The incidence of postoperative nausea was reduced after total i.v. compared with inhalation anaesthesia at 90 min (7 vs 35%, P<0.001), and 24 h (33 vs 52%, P=0.001). Conclusion. Total i.v. anaesthesia improves early postoperative patient well‐being and reduces the incidence of PONV. Br J Anaesth 2003; 91: 631-

Machine learning aplicado a remote sensing: aplicaciones en gobernanza digital para el desarrollo sustentable

El presente trabajo muestra las problemáticas abordadas mediante tecnologías de Machine Learning aplicadas a Remote Sensing, las que pueden servir de soporte a la gobernanza digital para el desarrollo sustentable. Para ello se realiza una revisión bibliográfica de la utilización de estas dos tecnologías con el fin de mostrar cuáles son los avances alcanzados en el área, en qué se está trabajando y cuáles son las aplicaciones relacionadas con la gobernanza electrónica para el desarrollo sustentable.Trabajo presentado por el Instituto de Investigación y Transferencia en Tecnología (ITT