Distributed algorithms for global optimization on sparse networks of arbitrary bandwidths

The optimization of resource allocation in sparse networks with real variables is studied using methods of statistical physics. Efficient distributed algorithms are devised on the basis of insight gained from the analysis and are examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.

Inference and optimization of real edges on sparse graphs:A statistical physics perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

Stock selection with full-scale optimization and differential evolution

When composing stock portfolios, managers frequently choose among hundreds of stocks. The stocks' risk properties are analyzed with statistical tools, and managers try to combine these to meet the investors' risk profiles. A recently developed tool for performing such optimization is called full-scale optimization (FSO). This methodology is very flexible for investor preferences, but because of computational limitations it has until now been infeasible to use when many stocks are considered. We apply the artificial intelligence technique of differential evolution to solve FSO-type stock selection problems of 97 assets. Differential evolution finds the optimal solutions by self-learning from randomly drawn candidate solutions. We show that this search technique makes large scale problem computationally feasible and that the solutions retrieved are stable. The study also gives further merit to the FSO technique, as it shows that the solutions suit investor risk profiles better than portfolios retrieved from traditional methods.

Optimization of protein crystallization:the OptiCryst project

Protein crystallization has gained a new strategic and commercial relevance in the postgenomic era due to its pivotal role in structural genomics. Producing high quality crystals has always been a bottleneck to efficient structure determination, and this problem is becoming increasingly acute. This is especially true for challenging, therapeutically important proteins that typically do not form suitable crystals. The OptiCryst consortium has focused on relieving this bottleneck by making a concerted effort to improve the crystallization techniques usually employed, designing new crystallization tools, and applying such developments to the optimization of target protein crystals. In particular, the focus has been on the novel application of dual polarization interferometry (DPI) to detect suitable nucleation; the application of in situ dynamic light scattering (DLS) to monitor and analyze the process of crystallization; the use of UV-fluorescence to differentiate protein crystals from salt; the design of novel nucleants and seeding technologies; and the development of kits for capillary counterdiffusion and crystal growth in gels. The consortium collectively handled 60 new target proteins that had not been crystallized previously. From these, we generated 39 crystals with improved diffraction properties. Fourteen of these 39 were only obtainable using OptiCryst methods. For the remaining 25, OptiCryst methods were used in combination with standard crystallization techniques. Eighteen structures have already been solved (30% success rate), with several more in the pipeline.

Recombinant protein production in yeast:methods and protocols

In the last few years, significant advances have been made in understanding how a yeast cell responds to the stress of producing a recombinant protein, and how this information can be used to engineer improved host strains. The molecular biology of the expression vector, through the choice of promoter, tag and codon optimization of the target gene, is also a key determinant of a high-yielding protein production experiment. Recombinant Protein Production in Yeast: Methods and Protocols examines the process of preparation of expression vectors, transformation to generate high-yielding clones, optimization of experimental conditions to maximize yields, scale-up to bioreactor formats and disruption of yeast cells to enable the isolation of the recombinant protein prior to purification. Written in the highly successful Methods in Molecular Biology™ series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.

Optimization of anterior eye fluorescein viewing

Purpose: To optimize anterior eye fluorescein viewing and image capture. Design: Prospective experimental investigation. Methods: The spectral radiance of ten different models of slit-lamp blue luminance and the spectral transmission of three barrier filters were measured. Optimal clinical instillation of fluorescein was evaluated by a comparison of four different instillation methods of fluorescein into 10 subjects. Two methods used a floret, and two used minims of different concentration. The resulting fluorescence was evaluated for quenching effects and efficiency over time. Results: Spectral radiance of the blue illumination typically had an average peak at 460 nm. Comparison between three slit-lamps of the same model showed a similar spectral radiance distribution. Of the slit-lamps examined, 8.3% to 50.6% of the illumination output was optimized for >80% fluorescein excitation, and 1.2% to 23.5% of the illumination overlapped with that emitted by the fluorophore. The barrier filters had an average cut-off at 510 to 520 nm. Quenching was observed for all methods of fluorescein instillation. The moistened floret and the 1% minim reached a useful level of fluorescence in on average ∼20s (∼2.5× faster than the saturated floret and 2% minim) and this lasted for ∼160 seconds. Conclusions: Most slit-lamps' blue light and yellow barrier filters are not optimal for fluorescein viewing and capture. Instillation of fluorescein using a moistened floret or 1% minim seems most clinically appropriate as lower quantities and concentrations of fluorescein improve the efficiency of clinical examination. © 2006 Elsevier Inc. All rights reserved.

Message-passing for inference and optimization of real variables on sparse graphs

The inference and optimization in sparse graphs with real variables is studied using methods of statistical mechanics. Efficient distributed algorithms for the resource allocation problem are devised. Numerical simulations show excellent performance and full agreement with the theoretical results. © Springer-Verlag Berlin Heidelberg 2006.

Optimization of dispersion-managed optical fiber lines

We present various approaches to the optimization of optical fiber lines and discuss the ranges of validity of such methods. An effective scheme for upgrading of existing transmission lines using dispersion-management with optimization of the pre- and postcompensating fiber is examined. The theory and numerical methods are illustrated in application to the Upgrade of a specific installed Deutsche Telekom fiber line.

What are dynamic optimization problems?

Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.

Generalized Scalarizing Problems GENS and GENSLex of Multicriteria Optimization

* This paper is partially supported by the National Science Fund of Bulgarian Ministry of Education and Science under contract № I–1401\2004 "Interactive Algorithms and Software Systems Supporting Multicriteria Decision Making".

An Interactive Method of Linear Mixed Integer Multicriteria Optimization

The paper describes a learning-oriented interactive method for solving linear mixed integer problems of multicriteria optimization. The method increases the possibilities of the decision maker (DM) to describe his/her local preferences and at the same time it overcomes some computational difficulties, especially in problems of large dimension. The method is realized in an experimental decision support system for finding the solution of linear mixed integer multicriteria optimization problems.

Classification-Based Method of Linear Multicriteria Optimization

The paper describes a classification-based learning-oriented interactive method for solving linear multicriteria optimization problems. The method allows the decision makers describe their preferences with greater flexibility, accuracy and reliability. The method is realized in an experimental software system supporting the solution of multicriteria optimization problems.

Exact Discriminant Function Design Using Some Optimization Techniques

Some aspects of design of the discriminant functions that in the best way separate points of predefined final sets are considered. The concept is introduced of the nested discriminant functions which allow to separate correctly points of any of the final sets. It is proposed to apply some methods of non-smooth optimization to solve arising extremal problems efficiently.

2D Apportionment Methods

Михаил Константинов, Костадин Янев, Галина Пелова, Юлиана Бонева - В работата се разглеждат двумерни пропорционални изборни системи, при които броят на партийните мандати се определя на национално ниво, а персонификацията на мандатите става чрез регионални партийни листи. При това, броят на мандатите във всеки район се определя пропорционално на населението. Предложени са нови подобрени методи за двумерно разпределение и са представени резултати от числени пресмятания с данните от парламентарните избори през 2009 г.

Notes on the Solution of Dynamic Optimization Problems with Explicit Controls in Discrete-Time Economic Models

Йордан Йорданов, Андрей Василев - В работата се изследват методи за решаването на задачи на оптималното управление в дискретно време с безкраен хоризонт и явни управления. Дадена е обосновка на една процедура за решаване на такива задачи, базирана на множители на Лагранж, коята често се употребява в икономическата литература. Извеждени са необходимите условия за оптималност на базата на уравнения на Белман и са приведени достатъчни условия за оптималност при допускания, които често се използват в икономиката.