Global optimization and stochastic differential equations

Let ⁿ be then-dimensional real Euclidean space,x=(x ₁,x ₂, ...,x _n)^{T n} , and letf: ⁿ R be a real-valued function. We consider the problem of finding the global minimizers off. A new method to compute numerically the global minimizers by following the paths of a system of stochastic differential equations is proposed. This method is motivated by quantum mechanics. Some numerical experience on a set of test problems is presented. The method compares favorably with other existing methods for global optimization.This research has been supported by the European Research Office of the US Army under Contract No. DAJA-37-81-C-0740.The third author gratefully acknowledges Prof. A. Rinnooy Kan for bringing to his attention Ref. 4.     

通过脱碳控制全球平均温度

Establishing a reliable method to predict the global mean temperature (T_e) is of great importance because CO₂ reduction activities require political and global cooperation and significant financial resources. The current climate models all seem to predict that the earth's temperature will continue to increase, mainly based on the assumption that CO₂ emissions cannot be lowered significantly in the foreseeable future. Given the earth's multifactor climate system, attributing atmospheric CO₂ as the only cause for the observed temperature anomaly is most likely an oversimplification; the presence of water (H₂O) in the atmosphere should at least be considered. As such, T_e is determined by atmospheric water content controlled by solar activity, along with anthropogenic CO₂ activities. It is possible that the anthropogenic CO₂ activities can be reduced in the future. Based on temperature measurements and thermodynamic data, a new model for predicting T_e has been developed. Using this model, past, current, and future CO₂ and H₂O data can be analyzed and the associated T_e calculated. This new, esoteric approach is more accurate than various other models, but has not been reported in the open literature. According to this model, by 2050, T_e may increase to 15.5 ℃ under "business-as-usual" emissions. By applying a reasonable green technology activity scenario, T_e may be reduced to approximately 14.2 ℃. To achieve CO₂ reductions, the scenario described herein predicts a CO₂ reduction potential of 513 gigatons in 30 years. This proposed scenario includes various CO₂ reduction activities, carbon capturing technology, mineralization, and bio-char production; the most important CO₂ reductions by 2050 are expected to be achieved mainly in the electricity, agriculture, and transportation sectors. Other more aggressive and plausible drawdown scenarios have been analyzed as well, yielding CO₂ reduction potentials of 1051 and 1747 gigatons, respectively, in 30 years, but they may reduce global food production. It is emphasized that the causes and predictions of the global warming trend should be regarded as open scientific questions because several details concerning the physical processes associated with global warming remain uncertain. For example, the role of solar activities coupled with Milankovitch cycles are not yet fully understood. In addition, other factors, such as ocean CO₂ uptake and volcanic activity, may not be negligible.     

The circular chemistry conceptual framework: A way forward to sustainability in industry 4.0

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The domain of partial attraction of a Poisson law

Groshev gave a characterization of the union of domains of partial attraction of all Poisson laws in 1941. His classical condition is expressed by the underlying distribution function and disguises the role of the mean of the attracting distribution. In the present paper we start out from results of the recent probabilistic approach and derive characterizations for any fixed >0 in terms of the underlying quantile function. The approach identifies the portion of the sample that contributes the limiting Poisson behavior of the sum, delineates the effect of extreme values, and leads to necessary and sufficient conditions all involving . It turns out that the limiting Poisson distributions arise in two qualitatively different ways depending upon whether >1 or <1. A concrete construction, illustrating all the results, also shows that in the boundary case when =1 both possibilities may occur.     

On Timonov's algorithm for global optimization of univariate Lipschitz functions

Timonov proposes an algorithm for global maximization of univariate Lipschitz functions in which successive evaluation points are chosen in order to ensure at each iteration a maximal expected reduction of the region of indeterminacy, which contains all globally optimal points. It is shown that such an algorithm does not necessarily converge to a global optimum.     

Global optimization of univariate Lipschitz functions: II. New algorithms and computational comparison

We consider the following global optimization problems for a Lipschitz functionf implicitly defined on an interval [a, b]. Problem P: find a globally-optimal value off and a corresponding point; Problem Q: find a set of disjoint subintervals of [a, b] containing only points with a globally-optimal value and the union of which contains all globally optimal points. A two-phase algorithm is proposed for Problem P. In phase I, this algorithm obtains rapidly a solution which is often globally-optimal. Moreover, a sufficient condition onf for this to be the case is given. In phase II, the algorithm proves the-optimality of the solution obtained in phase I or finds a sequence of points of increasing value containing one with a globally-optimal value. The new algorithm is empirically compared (on twenty problems from the literature) with a best possible algorithm (for which the optimal value is assumed to be known), with a passive algorithm and with the algorithms of Evtushenko, Galperin, Shen and Zhu, Piyavskii, Timonov and Schoen. For small, the new algorithm requires only a few percent more function evaluations than the best possible one. An extended version of Piyavskii's algorithm is proposed for problem Q. A sufficient condition onf is given for the globally optimal points to be in one-to-one correspondance with the obtained intervals. This result is achieved for all twenty test problems.The research of the authors has been supported by AFOSR grants 0271 and 0066 to Rutgers University. Research of the second author has been also supported by NSERC grant GP0036426, FCAR grant 89EQ4144 and partially by AFOSR grant 0066. We thank Nicole Paradis for her help in drawing the figures.     

A review of statistical models for global optimization

A review of statistical models for global optimization is presented. Rationality of the search for a global minimum is formulated axiomatically and the features of the corresponding algorithm are derived from the axioms. Furthermore the results of some applications of the proposed algorithm are presented and the perspectives of the approach are discussed.     

Properties of the random search in global optimization

From theorems which we prove about the behavior of gaps in a set ofN uniformly random points on the interval [0, 1], we determine properties of the random search procedure in one-dimensional global optimization. In particular, we show that the uniform grid search is better than the random search when the optimum is chosen using the deterministic strategy, that a significant proportion of large gaps are contained in the uniformly random search, and that the error in the determination of the point at which the optimum occurs, assuming that it is unique, will on the average be twice as large using the uniformly random search compared with the uniform grid. In addition, some of the properties of the largest gap are verified numerically, and some extensions to higher dimensions are discussed. The latter show that not all of the conclusions derived concerning the inadequacies of the one-dimensional random search extend to higher dimensions, and thaton average the random search is better than the uniform grid for dimensions greater than 6.This paper is based on work started in the Statistics Department of Princeton University when the first author was visiting as a Research Associate. Part of this research was supported by the Office of Naval Research, Contract No. 0014-67-A-0151-0017, and by the US Army Research Office—Durham, Contract No. DA-31-124-ARO-D-215.2 The authors wish to thank B. Omodei for his careful work in preparing the programs for the results of Figs. 1–2 and Table 1. The computations were performed on the IBM 360/50 of the Australian National University's Computer Centre. Thanks are also due to R. Miles for suggestions regarding the extension of the results to multidimensional regions, and to P. A. P. Moran and R. Brent for suggestions regarding the evaluation of the integral ₀ ¹ ... ^0/1 (x ₁ ² + ... +x _n ^/2 )^1/2 dx ₁ ...dx _n.     

Equilibrium,pseudoequilibrium and sliding-mode heteroclinic orbit in a Filippov-type plant disease model

Plant diseases have caused tremendous crop losses and have massive impacts on food security and environment. Modeling the spread of plant diseases and understanding the dynamics of the resulting plant disease models may provide practical insights on designing effective control measures. In this paper, by incorporating cultural strategies and economic threshold policy, we present a Filippov-type plant disease model. The resulting model has state dependent discontinuous right-hand side and thus non-smooth analysis and generalized Lyapunov approach are employed for model analysis. We show that the model exhibits the phenomena of stable equilibrium, unstable pseudoequilibrium as well as sliding-mode heteroclinic orbit. Biological implications of our results in implementing control strategies for plant diseases are also discussed.     

On the nonlinear stochastic realization problem

In this paper we construct a first solution of the stochastic realization problem in a nonlinear setting. The great bulk of previous work on stochastic realization has been in the linear Gaussian setting. Such Markovian representations are used e.g., to apply certain filtering and stochastic control techniques. Our methods consist of an amalgamation of methods introduced by Nelson with the Lax-Phillips type geometric approach to linear Gaussian stochastic realization which has been developed by Lindquist and Picci and by Ruckebusch. The result of this that we are able to realize any purely nondeterministic process satisfying suitable continuity conditions as an instantaneous function of a Markov process.