Optimizing Positively Dominated Systems

It has recently been shown that several classical open problems in linear system theory, such as optimization of decentralized output feedback controllers, can be readily solved for positive systems using linear programming. In particular, optimal solutions can be verified for large-scale systems using computations that scale linearly with the number of interconnections. Hence two fundamental advantages are achieved compared to classical methods for multivariable control: Distributed implementations and scalable computations. This paper extends these ideas to the class of positively dominated systems. The results are illustrated by computation of optimal spring constants for a network of point-masses connected by springs

Multi-objective model for optimizing railway infrastructure asset renewal

Trabalho inspirado num problema real da empresa Infraestruturas de Portugal, EP.A multi-objective model for managing railway infrastructure asset renewal is presented. The model aims to optimize three objectives, while respecting operational constraints: levelling investment throughout multiple years, minimizing total cost and minimizing work start postponements. Its output is an optimized intervention schedule. The model is based on a case study from a Portuguese infrastructure management company, which specified the objectives and constraints, and reflects management practice on railway infrastructure. The results show that investment levelling greatly influences the other objectives and that total cost fluctuations may range from insignificant to important, depending on the condition of the infrastructure. The results structure is argued to be general and suggests a practical methodology for analysing trade-offs and selecting a solution for implementation.info:eu-repo/semantics/publishedVersio

Cross-talk and interference enhance information capacity of a signaling pathway

A recurring motif in gene regulatory networks is transcription factors (TFs) that regulate each other, and then bind to overlapping sites on DNA, where they interact and synergistically control transcription of a target gene. Here, we suggest that this motif maximizes information flow in a noisy network. Gene expression is an inherently noisy process due to thermal fluctuations and the small number of molecules involved. A consequence of multiple TFs interacting at overlapping binding-sites is that their binding noise becomes correlated. Using concepts from information theory, we show that in general a signaling pathway transmits more information if 1) noise of one input is correlated with that of the other, 2) input signals are not chosen independently. In the case of TFs, the latter criterion hints at up-stream cross-regulation. We demonstrate these ideas for competing TFs and feed-forward gene regulatory modules, and discuss generalizations to other signaling pathways. Our results challenge the conventional approach of treating biological noise as uncorrelated fluctuations, and present a systematic method for understanding TF cross-regulation networks either from direct measurements of binding noise, or bioinformatic analysis of overlapping binding-sites.Comment: 28 pages, 5 figure

A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost

Due to reducing the reliance of buildings on fossil fuels, Passive House (PH) is receiving more and more attention. It is important that integrated optimization of passive performance by considering energy demand, cost and thermal comfort. This paper proposed a set three-stage multi-objective optimization method that combines redundancy analysis (RDA), Gradient Boosted Decision Trees (GBDT) and Non-dominated sorting genetic algorithm (NSGA-II) for PH design. The method has strong engineering applicability, by reducing the model complexity and improving efficiency. Among then, the GBDT algorithm was first applied to the passive performance optimization of buildings, which is used to build meta-models of building performance. Compared with the commonly used meta-model, the proposed models demonstrate superior robustness with the standard deviation at 0.048. The optimization results show that the energy-saving rate is about 88.2% and the improvement of thermal comfort is about 37.8% as compared to the base-case building. The economic analysis, the payback period were used to integrate initial investment and operating costs, the minimum payback period and uncomfortable level of Pareto frontier solution are 0.48 years and 13.1%, respectively. This study provides the architects rich and valuable information about the effects of the parameters on the different building performance

Linear Convergence on Positively Homogeneous Functions of a Comparison Based Step-Size Adaptive Randomized Search: the (1+1) ES with Generalized One-fifth Success Rule

In the context of unconstraint numerical optimization, this paper investigates the global linear convergence of a simple probabilistic derivative-free optimization algorithm (DFO). The algorithm samples a candidate solution from a standard multivariate normal distribution scaled by a step-size and centered in the current solution. This solution is accepted if it has a better objective function value than the current one. Crucial to the algorithm is the adaptation of the step-size that is done in order to maintain a certain probability of success. The algorithm, already proposed in the 60's, is a generalization of the well-known Rechenberg's $(1+1)$ Evolution Strategy (ES) with one-fifth success rule which was also proposed by Devroye under the name compound random search or by Schumer and Steiglitz under the name step-size adaptive random search. In addition to be derivative-free, the algorithm is function-value-free: it exploits the objective function only through comparisons. It belongs to the class of comparison-based step-size adaptive randomized search (CB-SARS). For the convergence analysis, we follow the methodology developed in a companion paper for investigating linear convergence of CB-SARS: by exploiting invariance properties of the algorithm, we turn the study of global linear convergence on scaling-invariant functions into the study of the stability of an underlying normalized Markov chain (MC). We hence prove global linear convergence by studying the stability (irreducibility, recurrence, positivity, geometric ergodicity) of the normalized MC associated to the $(1+1)$-ES. More precisely, we prove that starting from any initial solution and any step-size, linear convergence with probability one and in expectation occurs. Our proof holds on unimodal functions that are the composite of strictly increasing functions by positively homogeneous functions with degree $\alpha$ (assumed also to be continuously differentiable). This function class includes composite of norm functions but also non-quasi convex functions. Because of the composition by a strictly increasing function, it includes non continuous functions. We find that a sufficient condition for global linear convergence is the step-size increase on linear functions, a condition typically satisfied for standard parameter choices. While introduced more than 40 years ago, we provide here the first proof of global linear convergence for the $(1+1)$-ES with generalized one-fifth success rule and the first proof of linear convergence for a CB-SARS on such a class of functions that includes non-quasi convex and non-continuous functions. Our proof also holds on functions where linear convergence of some CB-SARS was previously proven, namely convex-quadratic functions (including the well-know sphere function)

[Corrigendum to] Effects of small-scale turbulence on lower trophic levels under different nutrient conditions [vol 32, pg 197, 2010]

Small-scale turbulence affects the pelagic food web and energy flow in marine systems and the impact is related to nutrient conditions and the assemblage of organisms present. We generated five levels of turbulence (2*10 29 to 1*10 24 W kg 21 ) in land-based mesocosms (volume 2.6 m 3 ) with and without additional nutrients (31:16:1 Si:N:P m M) to asses the effect of small-scale turbulence on the lower part of the pelagic food web under different nutrient conditions. The ecological influence of nutrients and small-scale turbulence on lower trophic levels was quantified using multivariate statistics (RDA), where nutrients accounted for 31.8% of the observed biological variation, while 7.2% of the variation was explained by small-scale turbulence and its interaction with nutrients. Chlorophyll a, primary production rates, bacterial production rates and diatom and dinoflagellate abundance were positively correlated to turbulence, regardless of nutrient conditions. Abundance of autotrophic flagellates, total phytoplankton and bacteria were positively correlated to turbulence only when nutrients were added. Impact of small-scale turbulence was related to nutrient con- ditions, with implications for oligotrophic and eutrophic situations. The effect on community level was also different compared to single species level. Microbial processes drive biogeochemical cycles, and nutrient-controlled effects of small-scale turbulence on such processes are relevant to foresee altered carbon flow in marine systems

HARVESTING STRATEGIES IN A FISH STOCK DOMINATED BY LOW-FREQUENCY VARIABILITY: THE NORWEGIAN SPRING-SPAWNING HERRING (CLUPEA HARENGUS)

Heavy positively autocorrelated natural fluctuations in a fisheries stock level are problematic for fisheries management, and collapses in the stock dynamics are difficult to avoid. In this paper, we compare three different harvesting strategies (proportional harvesting, threshold harvesting, and proportional threshold harvesting) in an autocorrelated and heavily fluctuating fishery — the Norwegian spring-spawning herring (Clupea harengus) — in terms of risk of quasi-extinction, average annual yield, and coefficient of variation of the yield. Contrary to general expectations, we found that the three strategies produce comparable yields and risks of quasi-extinction. The only observable difference was slightly higher yield and variation in the proportional threshold strategy when the yield is optimized. Thus, it remains an open question as how to characterize the circumstances when it is particularly needful to apply threshold levels in harvest policies.Resource /Energy Economics and Policy,