Concurrent Detection of Protein Adsorption on Mixed Nanoparticles by Differential Centrifugal Sedimentation

In mixtures of nanoparticles of various sizes or compositions, monitoring protein partitioning on their surfaces provides important information about particle–protein interactions during competitive adsorption. Utilizing the size‐resolving capability of differential centrifugal sedimentation, the adsorption of bovine serum albumin on multisize gold nanoparticles with diameters ranging from 20 to 100 nm or gold, silver, and silica nanoparticles with similar diameter can be concurrently observed. This method can be used to gain insight into nanoparticle–protein interactions based on analyses of curvature and relative abundance.     

Colorimetric Detection of Fe(II) and Co(II) by Using Terpyridine-Based Derivative

Journal of Applied Spectroscopy - A multi-ion chromogenic sensor based on a terpyridine moiety was developed for the semiquantitative, visual, and sensitive speciation analysis of Fe2+ and Co2+...     

Sparse representation-based image restoration via nonlocal supervised coding

Sparse representation (SR) and nonlocal technique (NLT) have shown great potential in low-level image processing. However, due to the degradation of the observed image, SR and NLT may not be accurate enough to obtain a faithful restoration results when they are used independently. To improve the performance, in this paper, a nonlocal supervised coding strategy-based NLT for image restoration is proposed. The novel method has three main contributions. First, to exploit the useful nonlocal patches, a nonnegative sparse representation is introduced, whose coefficients can be utilized as the supervised weights among patches. Second, a novel objective function is proposed, which integrated the supervised weights learning and the nonlocal sparse coding to guarantee a more promising solution. Finally, to make the minimization tractable and convergence, a numerical scheme based on iterative shrinkage thresholding is developed to solve the above underdetermined inverse problem. The extensive experiments validate the effectiveness of the proposed method.     

The SC property of an expected residual function arising from stochastic complementarity problems

The stochastic nonlinear complementarity problem has been recently reformulated as an expected residual minimization problem which minimizes an expected residual function defined by an NCP function. In this work, we show that the expected residual function defined by the Fischer–Burmeister function is an function.     

Singular values of a real rectangular tensor

Real rectangular tensors arise from the strong ellipticity condition problem in solid mechanics and the entanglement problem in quantum physics. In this paper, we systematically study properties of singular values of a real rectangular tensor, and give an algorithm to find the largest singular value of a nonnegative rectangular tensor. Numerical results show that the algorithm is efficient.     

Polynomiality of an inexact infeasible interior point algorithm for semidefinite programming

In this paper we present a primal-dual inexact infeasible interior-point algorithm for semidefinite programming problems (SDP). This algorithm allows the use of search directions that are calculated from the defining linear system with only moderate accuracy, and does not require feasibility to be maintained even if the initial iterate happened to be a feasible solution of the problem. Under a mild assumption on the inexactness, we show that the algorithm can find an -approximate solution of an SDP in O(n²ln(1/)) iterations. This bound of our algorithm is the same as that of the exact infeasible interior point algorithms proposed by Y. Zhang.Research supported in part by the Singapore-MIT alliance, and NUS Academic Research Grant R-146-000-032-112.Mathematics Subject Classification (1991): 90C05, 90C30, 65K05     

A Modified SQP Method and Its Global Convergence

The sequential quadratic programming method developed by Wilson, Han andPowell may fail if the quadratic programming subproblems become infeasibleor if the associated sequence of search directions is unbounded. In [1], Hanand Burke give a modification to this method wherein the QP subproblem isaltered in a way which guarantees that the associated constraint region isnonempty and for which a robust convergence theory is established. In thispaper, we give a modification to the QP subproblem and provide a modifiedSQP method. Under some conditions, we prove that the algorithm eitherterminates at a Kuhn–Tucker point within finite steps or generates aninfinite sequence whose every cluster is a Kuhn–Tucker point.Finally, we give some numerical examples.     

半无限规划问题的一个有效解法

1．引言在计算机辅助设计和工程设计中,经常遇到下面的两类优化问题1,2］．1．无约束半无限极大极小问题．其中外x）二——x。。Im。x。。。Yi夕（x;N）这里J二（】,2,·,｝对任何7E八岁：R－xR”。＋R是连续可微的函数,X是R”。中的一个紧子集,且VYj）一O,这里问h）表示X体积．2．约束半无限代化问题．其中I一（1,2,·．小记L二《0｝UI对任何jCL冲’（x）一max。。。Yi夕（x,yi）·这里拉：PX＊n＋R是连续可微函数,X是”。中的一个紧子集,且NU）一0·注．设Y（Z,一二切EyW一叫卜4．今后对本文用到的紧子集地做…     

Convergence Analysis of an Infeasible Interior Point Algorithm Based on a Regularized Central Path for Linear Complementarity Problems

Most existing interior-point methods for a linear complementarity problem (LCP) require the existence of a strictly feasible point to guarantee that the iterates are bounded. Based on a regularized central path, we present an infeasible interior-point algorithm for LCPs without requiring the strict feasibility condition. The iterates generated by the algorithm are bounded when the problem is a P _* LCP and has a solution. Moreover, when the problem is a monotone LCP and has a solution, we prove that the convergence rate is globally linear and it achieves `-feasibility and `-complementarity in at most O(n ² ln(1/`)) iterations with a properly chosen starting point.