Constraint-Coupled Distributed Optimization: A Relaxation and Duality Approach

In this paper, we consider a general challenging distributed optimization setup arising in several important network control applications. Agents of a network want to minimize the sum of local cost functions, each one depending on a local variable, subject to local and coupling constraints, with the latter involving all the decision variables. We propose a novel fully distributed algorithm based on a relaxation of the primal problem and an elegant exploration of duality theory. Despite its complex derivation, based on several duality steps, the distributed algorithm has a very simple and intuitive structure. That is, each node finds a primal-dual optimal solution pair of a local relaxed version of the original problem and then updates suitable auxiliary local variables. We prove that agents asymptotically compute their portion of an optimal (feasible) solution of the original problem. This primal recovery property is obtained without any averaging mechanism typically used in dual decomposition methods. To corroborate the theoretical results, we show how the methodology applies to an instance of a distributed model-predictive control scheme in a microgrid control scenario

Distributed Primal Decomposition for Large-Scale MILPs

This paper deals with a distributed Mixed-Integer Linear Programming (MILP) set-up arising in several control applications. Agents of a network aim to minimize the sum of local linear cost functions subject to both individual constraints and a linear coupling constraint involving all the decision variables. A key, challenging feature of the considered set-up is that some components of the decision variables must assume integer values. The addressed MILPs are NP-hard, nonconvex and large-scale. Moreover, several additional challenges arise in a distributed framework due to the coupling constraint, so that feasible solutions with guaranteed suboptimality bounds are of interest. We propose a fully distributed algorithm based on a primal decomposition approach and an appropriate tightening of the coupling constraint. The algorithm is guaranteed to provide feasible solutions in finite time. Moreover, asymptotic and finite-time suboptimality bounds are established for the computed solution. Montecarlo simulations highlight the extremely low suboptimality bounds achieved by the algorithm

Enhanced gradient tracking algorithms for distributed quadratic optimization via sparse gain design

In this paper we propose a new control-oriented design technique to enhance the algorithmic performance of the distributed gradient tracking algorithm. We focus on a scenario in which agents in a network aim to cooperatively minimize the sum of convex, quadratic cost functions depending on a common decision variable. By leveraging a recent system-theoretical reinterpretation of the considered algorithmic framework as a closed-loop linear dynamical system, the proposed approach generalizes the diagonal gain structure associated to the existing gradient tracking algorithms. Specifically, we look for closed-loop gain matrices that satisfy the sparsity constraints imposed by the network topology, without however being necessarily diagonal, as in existing gradient tracking schemes. We propose a novel procedure to compute stabilizing sparse gain matrices by solving a set of nonlinear matrix inequalities, based on the solution of a sequence of approximate linear versions of such inequalities. Numerical simulations are presented showing the enhanced performance of the proposed design compared to existing gradient tracking algorithms

Distributed Personalized Gradient Tracking with Convex Parametric Models

We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user. The local objective functions are assumed to have a composite structure and to consist of a known time-varying (engineering) part and an unknown (user-specific) part. Regarding the unknown part, it is assumed to have a known parametric (e.g., quadratic) structure a priori, whose parameters are to be learned along with the evolution of the algorithm. The algorithm is composed of two intertwined components: (i) a dynamic gradient tracking scheme for finding local solution estimates and (ii) a recursive least squares scheme for estimating the unknown parameters via user's noisy feedback on the local solution estimates. The algorithm is shown to exhibit a bounded regret under suitable assumptions. Finally, a numerical example corroborates the theoretical analysis

Automatic Detection of Local Cloud Systems from MODIS Data

Abstract This paper describes an algorithm that is aimed at the identification of cloudy and clear pixels in Moderate-Resolution Imaging Spectroradiometer (MODIS) images to support earth science and nowcasting applications. The process from geolocated and calibrated data allows one to obtain cloud masks with four clear-sky confidence levels for five different cloud system types. The technique has been developed using the MODIS cloud-mask algorithm heritage, but the threshold tests performed have been executed without comparing solar reflectances and thermal brightness temperatures with thresholds determined in advance, but instead with thresholds carried out from classification methods. The main advantage of this technique is that the thresholds are obtained directly from the images. Seventy-five percent of the spectral signatures (known as end members) derived from the winter images in the detection of the various cloud types and 80% of the summer end members can be considered as being well discriminated. Furthermore, it seems that the end members characterizing the different cloud systems are constant throughout the various seasons of the year (they vary with a confidence level of 60%), whereas those describing clear sky change in a notable manner (the associated confidence level is 99%). The algorithm is able to produce cloud masks pertinent to limited regions at a mesoscale level, which may be a key factor for nowcasting purposes. This work shows that the use of end members and spectral angles, as opposed to spectral thresholds, should be carefully examined because of the fact that it might be simpler or that higher performances may be achieved at a regional scale

Hybrid near-optimum binary receiver with realistic photon-number-resolving detectors

We propose a near-optimum receiver for the discrimination of binary phase-shift-keyed coherent states employing photon-number-resolving detectors. The receiver exploits a discrimination strategy based on both the so-called homodyne-like and the direct detection, thus resulting in a hybrid scheme. We analyse the performance and the robustness of the proposed scheme under realistic conditions, namely, in the presence of inefficient detection and dark counts. We show that the present hybrid setup is near-optimum and beats both the standard-quantum-limit and the performance of the Kennedy receiver.Comment: 20 pages, 6 figure

Dirofilariosis canina: microfilaremia en perros de la ribera del Río de la Plata, Argentina

La dirofilariosis es una enfermedad zoonótica causada por Dirofilaria immitis y transmitida por mosquitos. Este trabajo presenta un relevamiento de la microfilaremia en perros en la ribera del Río de la Plata utilizando la técnica de Knott modificada y estudia posibles especies de mosquitos como vectores. A través de consultorios veterinarios y centros de zoonosis de Villa Domínico, Quilmes Este, La Plata y Berisso (Buenos Aires) se obtuvieron 265 muestras de sangre de perros y se completó una encuesta (sexo, edad, pedigree, talla, largo del pelo, manejo de las mascotas y tratamiento con ivermectina). Se capturaron y disecaron 412 mosquitos con aspirador manual sobre cebo humano en la ribera del Río de la Plata para observar formas filariformes. El 78,8% de las muestras de sangre correspondieron a hembras, el 76,2% de los perros fueron mestizos, las tallas chicas a medianas (30,1% y 41,1% respectivamente) y el 38,2% de las mascotas permanecía las 24 horas fuera de la vivienda. El 73% de las muestras correspondieron a perros menores de 6 años. Se detectaron 6 casos Knott positivos (prevalencia 2,26%). El 50% de los casos positivos eran asintomáticos. Si bien los machos estuvieron más parasitados que las hembras (diferencias significativas), se estima que ello está asociado a un manejo especial de las mascotas hembras por parte de los dueños. Éstas suelen permanecer mayor tiempo en el interior de la vivienda y así tendrían menor probabilidad de contacto con mosquitos. Se observaron diferencias significativas entre edades. Los adultos estuvieron más parasitados que los jóvenes. No se hallaron formas filariformes en los mosquitos disecados. Se recomienda el test de Knott modificado como técnica rápida, económica y efectiva para el diagnóstico de dirofilariosis, siempre que el canino no haya sido tratado con ivermectin

New Variant of the Treatment of Acromion-Clavicular Dislocation With TightRope ® System in a Mini - Open Approach: A Preliminary Clinical Study

Background: Many different surgical techniques have been described to stabilize the acromion-clavicular (AC) dislocations. So far many of these procedures are performed only in arthroscopy. Objectives: In this study, we describe a new technique that utilizes the tightrope with a mini-invasive open approach for the acute stabilization of the acromion-clavicular joint (ACJ) dislocation. Patients and Methods: We set an prospective study aimed to verify the efficacy of this new surgical technique. We treated 28 patients with acute ACJ dislocation with ACJ TightRope ® System with dual mini access. We retrospectively reviewed the data of 34 patients treated with arthroscopic technique. They were considered as the control group. Results: At 6 month’s follow-up, all the 28 patients showed a stable joint during clinical examination and obtained an average Constant score of 98.62/100, with a complete recovery of ROM and strength in abduction. The mean operation time was of 33.7 minutes. The mean recovery duration was 102.8 days. No significant difference was found between the experimental and control groups (P > 0.05). Conclusions: Results of this trial suggest the effectiveness of this new mini-invasive surgical technique in producing clinical and functional recovery in patients with ACJ dislocations