A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization

We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimization, which we call the LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for numerical optimization of non-linear, non-convex optimization problems in continuous domain. Inspired by the limited memory BFGS method of Liu and Nocedal (1989), the LM-CMA-ES samples candidate solutions according to a covariance matrix reproduced from $m$ direction vectors selected during the optimization process. The decomposition of the covariance matrix into Cholesky factors allows to reduce the time and memory complexity of the sampling to $O(mn)$, where $n$ is the number of decision variables. When $n$ is large (e.g., $n$ > 1000), even relatively small values of $m$ (e.g., $m=20,30$) are sufficient to efficiently solve fully non-separable problems and to reduce the overall run-time.Comment: Genetic and Evolutionary Computation Conference (GECCO'2014) (2014

Analysis of the Hydrogen-rich Magnetic White Dwarfs in the SDSS

We have calculated optical spectra of hydrogen-rich (DA) white dwarfs with magnetic field strengths between 1 MG and 1000 MG for temperatures between 7000 K and 50000 K. Through a least-squares minimization scheme with an evolutionary algorithm, we have analyzed the spectra of 114 magnetic DAs from the SDSS (95 previously published plus 14 newly discovered within SDSS, and five discovered by SEGUE). Since we were limited to a single spectrum for each object we used only centered magnetic dipoles or dipoles which were shifted along the magnetic dipole axis. We also statistically investigated the distribution of magnetic-field strengths and geometries of our sample.Comment: to appear in the proceedings of the 16th European Workshop on White Dwarfs, Barcelona, 200

Numerical Simulation of Magnetic Interactions in Polycrystalline YFeO3

The magnetic behavior of polycrystalline yttrium orthoferrite was studied from the experimental and theoretical points of view. Magnetization measurements up to 170 kOe were carried out on a single-phase YFeO3 sample synthesized from heterobimetallic alkoxides. The complex interplay between weak-ferromagnetic and antiferromagnetic interactions, observed in the experimental M(H) curves, was successfully simulated by locally minimizing the magnetic energy of two interacting Fe sublattices. The resulting values of exchange field (H_E = 5590 kOe), anisotropy field (H_A = 0.5 kOe) and Dzyaloshinsky-Moriya antisymmetric field (H_D = 149 kOe) are in good agreement with previous reports on this system.Comment: 26 pages, 9 figure

Noisy Optimization: Convergence with a Fixed Number of Resamplings

It is known that evolution strategies in continuous domains might not converge in the presence of noise. It is also known that, under mild assumptions, and using an increasing number of resamplings, one can mitigate the effect of additive noise and recover convergence. We show new sufficient conditions for the convergence of an evolutionary algorithm with constant number of resamplings; in particular, we get fast rates (log-linear convergence) provided that the variance decreases around the optimum slightly faster than in the so-called multiplicative noise model. Keywords: Noisy optimization, evolutionary algorithm, theory.Comment: EvoStar (2014

Biocompatibility issues with modern implants in bone: a review for clinical orthopaedics

Skeletal defects may result from traumatic, infectious, congenital or neoplastic processes and are considered to be a challenge for reconstructive surgery. Although the autologous bone graft is still the “gold standard”, there is continuing demand for bone substitutes because of associated disadvantages, such as limited supply and potential donor side morbidity [1]. This is not only true for indications in orthopedic and craniomaxillofacial surgeries, but also in repairing endodontic defects and in dental implantology. Before clinical use all new bone substitute materials have to be validated for their osseoconductive and - depending on the composition of the material also -inductive ability, as well as for their long-term biocompatibility in bone. Serving this purpose various bone healing models to test osteocompatibility and inflammatory potential of a novel material on one hand and, on the other hand, non-healing osseous defects to assess the healing potential of a bone substitute material have been developed. Sometimes the use of more than one implantation site can be helpful to provide a wide range of information about a new material [2]. Important markers for biocompatibility and inflammatory responses are the cell types appearing after the implantation of foreign material. There, especially the role of foreign body giant cells (FBGC) is discussed controversial in the pertinent literature, such that it is not clear whether their presence marks an incompatibility of the biomaterial, or whether it belongs to a normal degradation behavior of modern, resorbable biomaterials. This publication is highlighting the different views currently existing about the function of FBGC that appear in response to biomaterials at the implantation sites. A short overview of the general classes of biomaterials, where FBGC may appear as cellular response, is added for clarity, but may not be complete

Analysis of Different Types of Regret in Continuous Noisy Optimization

The performance measure of an algorithm is a crucial part of its analysis. The performance can be determined by the study on the convergence rate of the algorithm in question. It is necessary to study some (hopefully convergent) sequence that will measure how "good" is the approximated optimum compared to the real optimum. The concept of Regret is widely used in the bandit literature for assessing the performance of an algorithm. The same concept is also used in the framework of optimization algorithms, sometimes under other names or without a specific name. And the numerical evaluation of convergence rate of noisy algorithms often involves approximations of regrets. We discuss here two types of approximations of Simple Regret used in practice for the evaluation of algorithms for noisy optimization. We use specific algorithms of different nature and the noisy sphere function to show the following results. The approximation of Simple Regret, termed here Approximate Simple Regret, used in some optimization testbeds, fails to estimate the Simple Regret convergence rate. We also discuss a recent new approximation of Simple Regret, that we term Robust Simple Regret, and show its advantages and disadvantages.Comment: Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States. 201

A relevância da avaliação para o investimento social privado

O crescente interesse pelo uso estratégico da avaliação de programas e políticas sociais no Brasil tem sido marcado por avanços, mas também por inquietações entre formuladores, gestores e avaliadores sobre suas escolhas metodológicas. Qual método é mais adequado para avaliar este ou aquele programa? Qual abordagem é mais precisa? Que método é mais sensível à realidade social? Que percurso avaliativo é capaz de exercer maior influência sobre a tomada de decisões? Lançando mão de reflexões teórica e casos práticos, somando experiências estrangeiras e brasileiras, o livro oferece respostas a estas perguntas, capazes de alavancar a capacidade de escolher métodos que melhor dialoguem com a realidade social, bem como com suas próprias premissas éticas, políticas e técnicas. Fruto do II Seminário Internacional "A Relevância da Avaliação para o Investimento Social Privado: Metodologias", iniciativa da Fundação Itaú Social, Fundação Roberto Marinho, Fundação Maria Cecília Souto Vidigal em parceria com a Move e apoio do GIFE e da Fundação Santillana, esta publicação é mais uma contribuição para o plural e, cada vez mais consistente, campo avaliativo do País