Convergence analysis and performance of the extended artificial physics optimization algorithm

This paper presents extended artificial physics optimization (EAPO), a population-based, stochastic, evolutionary algorithm (EA) for multidimensional search and optimization. EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm by including each individual’s best fitness history. Including the history improves EAPO’s search capability compared to APO. EAPO and APO invoke a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move individuals toward local and global optima. A proof of convergence is presented that reveals the conditions under which EAPO is guaranteed to converge. Discrete-time linear system theory is used to develop a second-order difference equation for an individual’s stochastic position vector as a function of time step. Stable solutions require eigenvalues inside the unit circle, leading to explicit convergence criteria relating the run parameters {m_i, w, G}. EAPO is tested against several benchmark functions with excellent results. The algorithm converges more quickly than APO and with better diversity.     

A Discussion of the Effect of Tortuosity on the Capillary Imbibition in Porous Media

In the past decades, there was considerable controversy over the Lucas–Washburn (LW) equation widely applied in capillary imbibition kinetics. Many experimental results showed that the time exponent of the LW equation is less than 0.5. Based on the tortuous capillary model and fractal geometry, the effect of tortuosity on the capillary imbibition in wetting porous media is discussed in this article. The average height growth of wetting liquid in porous media driven by capillary force following the [`(L)] _s(t) ~ t^1/2D_T{\overline L _{\rm {s}}(t)\sim t^{1/{2D_{\rm {T}}}}} law is obtained (here D _T is the fractal dimension for tortuosity, which represents the heterogeneity of flow in porous media). The LW law turns out to be the special case when the straight capillary tube (D _T = 1) is assumed. The predictions by the present model for the time exponent for capillary imbibition in porous media are compared with available experimental data, and the present model can reproduce approximately the global trend of variation of the time exponent with porosity changing.     

Accelerated method for optimization over density matrices in quantum state estimation

In this paper, a class of minimization problems over density matrices arising in the quantum state estimation is investigated. By making use of the Nesterov’s accelerated strategies, we introduce a modified augmented Lagrangian method to solve it, where the subproblem is tackled by the projected Barzilai–Borwein method with nonmonotone line search. Compared with the existing projected gradient method, several numerical examples are tested to show that the proposed method is efficient and promising.     

A parameterized proximal point algorithm for separable convex optimization

In this paper, we develop a parameterized proximal point algorithm (P-PPA) for solving a class of separable convex programming problems subject to linear and convex constraints. The proposed algorithm is provable to be globally convergent with a worst-case O(1 / t) convergence rate, where t denotes the iteration number. By properly choosing the algorithm parameters, numerical experiments on solving a sparse optimization problem arising from statistical learning show that our P-PPA could perform significantly better than other state-of-the-art methods, such as the alternating direction method of multipliers and the relaxed proximal point algorithm.     

A new model for sparse and low-rank matrix decomposition

The robust principal component analysis (RPCA) model is a popular method for solving problems with the nuclear norm and $\ell_1$ norm. However, it is time-consuming since in general one has to use the singular value decomposition in each iteration. In this paper, we introduce a novel model to reformulate the existed model by making use of low-rank matrix factorization to surrogate the nuclear norm for the sparse and low-rank decomposition problem. In such case we apply the Penalty Function Method (PFM) and Augmented Lagrangian Multipliers Method (ALMM) to solve this new non-convex optimization problem. Theoretically, corresponding to our methods, the convergence analysis is given respectively. Compared with classical RPCA, some practical numerical examples are simulated to show that our methods are much better than RPCA.     

Editorial to the Special Issue: Modeling and Characterization of Low Permeability (Tight) and Nanoporous Reservoirs

Transport in Porous Media -     

BINOL-phosphoric acids-catalyzed furylogous pinacol rearrangement of 1-[5-(hydroxy-diaryl-methyl)-furan-2-yl]-cyclobutanols into spiro cyclopentanones

A BINOL-phosphoric acid-catalyzed furylogous Pinacol rearrangement is developed. This reaction produces spiro cyclopentanone in good yields under mild conditions. The enantioselective version of this reaction is also investigated using chiral phosphoric acid (CPA) as the catalyst.     

固定化酶在生物医学上的应用(Ⅰ)——聚丙烯酰胺固定化尿酶的制备及性能

本文以交联聚丙烯酰胺对尿酶包埋进行了研究,制备出了球形固定化尿酶,其含酶量高(1份酶对1份聚合物),相对活性为50%左右,对尿素的分解活性:120m/ml(固定化尿酶,37℃,2小时),提高了稳定性,在4℃下,66天后活性降低8%,106天后活性降低22%,37℃下11天后活性降低18%,并且不泄漏酶。基于以上特点,该固定化尿酶为吸附型人工肾提供了一个较合适的活性材料。     

农药化学中的绿色化学

绿色化学是从根本上消除环境污染的必由之路。由于某些传统农药是引起环境污染的一个重要因素,因而对农药化学中的绿色化学进行研究和推广具有非常重要的意义。本文应用绿色化学原理,对农药化学中的一些绿色化学方法和技术作了简单介绍。     

多孔介质自发渗吸关键问题与思考

多孔介质自发渗吸现象普遍存在于自然界和工业应用领域, 深入研究其微观流动机理及宏观渗流规律具有重要的科学和工程价值。由于受多孔介质物理属性、流体性质、边界条件等多种因素的影响, 自发渗吸机理及渗流规律难以厘清。目前, 已有众多物理实验、理论分析及数值模拟等方法对多因素影响下的自发渗吸过程进行了广泛而深入的研究。本文就该领域存在的关键问题及最新进展进行梳理分析并提出若干思考, 以期对多孔介质自发渗吸的理解和未来研究工作提供一定的借鉴。