Adaptive Consensus: A network pruning approach for decentralized optimization

We consider network-based decentralized optimization problems, where each node in the network possesses a local function and the objective is to collectively attain a consensus solution that minimizes the sum of all the local functions. A major challenge in decentralized optimization is the reliance on communication which remains a considerable bottleneck in many applications. To address this challenge, we propose an adaptive randomized communication-efficient algorithmic framework that reduces the volume of communication by periodically tracking the disagreement error and judiciously selecting the most influential and effective edges at each node for communication. Within this framework, we present two algorithms: Adaptive Consensus (AC) to solve the consensus problem and Adaptive Consensus based Gradient Tracking (AC-GT) to solve smooth strongly convex decentralized optimization problems. We establish strong theoretical convergence guarantees for the proposed algorithms and quantify their performance in terms of various algorithmic parameters under standard assumptions. Finally, numerical experiments showcase the effectiveness of the framework in significantly reducing the information exchange required to achieve a consensus solution.Comment: 35 pages, 3 figure

Environmentally persistent pharmaceutical pollutants (EPPPs) in Brisbane river sediments and their relationship to urbanisation

The research project was aimed at investigating the relationship between urbanisation and the occurrence of ‘emerging organic pollutants ‘or ‘Environmentally Persistent Pharmaceutical Pollutants’ (EPPPs) in the Brisbane River sediments by developing improved test methods. The study was based on the detailed understanding of the pharmaceuticals most commonly consumed in South East Queensland and underpinned by the demographic data for the region and the urbanisation characteristics

Chemical Transformations Using Tungsten and Molybdenum Hydrogen Bronzes

Chemistr

Constraint-Based Local Search for Inventory Control Under Stochastic Demand and Lead Time

In this paper, we address the general multiperiod production/inventory problem with nonstationary stochastic demand and supplier lead time under service-level constraints. A replenishment cycle policy is modeled. We propose two hybrid algorithms that blend constraint programming and local search for computing near-optimal policy parameters. Both algorithms rely on a coordinate descent local search strategy; what differs is the way this strategy interacts with the constraint programming solver. These two heuristics are first, compared for small instances against an existing optimal solution method. Second, they are tested and compared with each other in terms of solution quality and run time on a set of larger instances that are intractable for the exact approach. Our numerical experiments show the effectiveness of our methods

Acid-base properties of Cu<SUB>1-x</SUB>Co<SUB>x</SUB>Fe<SUB>2</SUB>O<SUB>4</SUB> ferrospinels: FTIR investigations

Systematic IR spectroscopic studies were undertaken to investigate the acid-base properties of Cu-Co ferrospinels Cu1-xCoxFe2O4 (x=0 to 1) employed in phenol methylation to produce 2,6-xylenol. The IR spectra of the ferrospinels reveal that Fe3+ and Co2+ ions are mainly responsible for the various hydroxy groups on the surface. Temperature dependent IR studies of pyridine adsorbed on spinels and on the spinel phase with deliberately added metal oxide exemplify the contribution of the metal ions and their coordination state towards Lewis acidity. IR studies of the spinel surface with adsorbed CO2 and adsorption studies of electron acceptors such as 7,7,8,8-tetracyanoquinodimethane, 2,3,5,6-tetrachloro-1-4-benzoquinone and p-dinitrobenzene were carried out to evaluate the nature of the basic sites and the strength and distribution of the electron donor sites present on the spinel surface. It was found that the acidity (basicity) of the Cu1-xCoxFe2O4 spinel system increases (decreases) from x=0 to 1. A correlation between acidity, basicity and catalytic performance reveals that an intermediate acid-base character enhances the phenol methylation activity

Inflammatory pathways in the mechanism of parturition

Increasing evidence suggests that parturition is an inflammatory process. In this brief overview, inflammatory events occurring in association with parturition, and the mechanism by which they may contribute to labour and delivery will be discussed. Mention will be made of how this information may be of use in regulating the timing and the onset of parturition

Piecewise linear approximations for the static-dynamic uncertainty strategy in stochastic lot-sizing

In this paper, we develop mixed integer linear programming models to compute near-optimal policy parameters for the non-stationary stochastic lot sizing problem under Bookbinder and Tan's static-dynamic uncertainty strategy. Our models build on piecewise linear upper and lower bounds of the first order loss function. We discuss different formulations of the stochastic lot sizing problem, in which the quality of service is captured by means of backorder penalty costs, non-stockout probability, or fill rate constraints. These models can be easily adapted to operate in settings in which unmet demand is backordered or lost. The proposed approach has a number of advantages with respect to existing methods in the literature: it enables seamless modelling of different variants of the above problem, which have been previously tackled via ad-hoc solution methods; and it produces an accurate estimation of the expected total cost, expressed in terms of upper and lower bounds. Our computational study demonstrates the effectiveness and flexibility of our models.Comment: 38 pages, working draf