Verification of Positive Definiteness

We present a computational, simple and fast sufficient criterion to verify positive definiteness of a symmetric or Hermitian matrix. The criterion uses only standard floating-point operations in rounding to nearest, it is rigorous, it takes into account all possible computational and rounding errors, and is also valid in the presence of underflow. It is based on a floating-point Cholesky decomposition and improves a known result. Using the criterion an efficient algorithm to compute rigorous error bounds for the solution of linear systems with symmetric positive definite matrix follows. A computational criterion to verify that a given symmetric or Hermitian matrix is not positive definite is given as well. Computational examples demonstrate the effectiveness of our criteria. AMS subject classification (2000) 65G20, 15A18     

A Modified BFGS Algorithm for Unconstrained Optimization

In this paper we present a modified BFGS algorithm for unconstrainedoptimization. The BFGS algorithm updates an approximate Hessianwhich satisfies the most recent quasi-Newton equation. The quasi-Newtoncondition can be interpreted as the interpolation conditionthat the gradient value of the local quadratic model matchesthat of the objective function at the previous iterate. Ourmodified algorithm requires that the function value is matched,instead of the gradient value, at the previous iterate. Themodified algorithm preserves the global and local superlinearconvergence properties of the BFGS algorithm. Numerical resultsare presented, which suggest that a slight improvement has beenachieved.     

On the Positive Definiteness of Certain Functions

For a coinmutative senugoup (S, +, *) with involution and a function f : S → [0, ∞), the set S(f) of those p ≥ 0 such that f^P is a positive definite function on S is a closed subsemigroup of [0, ∞) containing 0. For S = (IR, +, x* = -x) it may happen that S(f) = { kd : k ∈ N₀ } for some d > 0, and it may happen that S(f) = {0} ? [d, ∞) for some d > O. If α > 2 and if S = (?, +, n* = -n) and f(n) = e^?[n]α or S = (IN₀, +, n* = n) and f(n) = e^nα, then S(f) ∪ (0, c) = ? and [d, ∞) ? S(f) for some d ≥; c > 0. Although (with c maximal and d minimal) we have not been able to show c = d in all cases, this equality does hold if S = ? and α ≥ 3.4. In the last section we give sinipler proofs of previously known results concerning the positive definiteness of x → e^?||x||α on normed spaces.     

一类改进BFGS算法及其收敛性分析

本文针对无约束最优化问题,基于目标函数的局部二次模型近似,提出一类改进的BFGS算法,称为 MBFGS算法。其修正 B_k的公式中含有一个参数θ∈[0,l],当 θ= 1时即得经典的BFGS公式;当θ∈[0、l）时,所得公式已不属于拟Newton类。在目标函数一致凸假设下,证明了所给算法的全局收敛性及局部超线性收敛性。     

On the Positive Definiteness of Cokriging Covariance Matrices

Variable-fidelity modeling (VFM), sometimes also termed multi-fidelity modeling, refers to the utilization of two or more data layers of different accuracy in order to construct an inexpensive emulator of a given numerical high-fidelity model. In practical applications, this situation arises when simulators of different accuracy for the same physical process are given and it is assumed that many low-fidelity sample points are affordable, but the high-fidelity model is extremely costly to assess. More precisely, the VFM objective is to construct a predictor function, which interpolates the primary sample data but is driven by the trend indicated by the secondary data. In view of the computational effort, the objective is to use as few high-fidelity sample points as possible to achieve a desired level of accuracy. A widely used VFM method is Cokriging. The technique yields the best linear unbiased estimator based on the given data, taking spatial correlation into account. One way to model spatial correlation is via positive definite correlation kernels that yield positive definite correlation matrices for distinct input sample points. In this contribution, we will address the positive definiteness of Cokriging correlation matrices which is necessary for the method but not granted, due to the modeling of the cross-correlations. We discuss both the cases of distinct and coincident low- and high-fidelity sample points. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Quantale矩阵的广义逆及其正定性

给出Quantale矩阵{1}-广义逆的一种刻划以及存在的条件,给出Quantale矩阵M-P广义逆的定义,讨论Quantale矩阵M-P广义逆的若干性质,得到Quantale矩阵M-P广义逆的具体形式.引入Quantale矩阵正定性的概念,研究交换幂等Quantale上矩阵正定的一些性质,得到交换幂等Quantale上矩阵正定的一些等价刻画.     

实正定矩阵的复合矩阵的正定性

本文讨论了实正定矩阵的复合矩阵的正定性，并且给出了实正定矩阵的复合矩阵仍为正定矩阵的一个充要条件.     

Characterization of Strict Positive Definiteness on products of complex spheres

Positivity - In this paper we consider Positive Definite functions on products $$\Omega _{2q}\times \Omega _{2p}$$ of complex spheres, and we obtain a condition, in terms of the coefficients in...     

广义Cauchy张量的正定性及互补问题的研究

广义Cauchy张量是由Cauchy张量推广而来,将在Cauchy张量的基础上,围绕广义Cauchy张量的正定性及共正性展开研究,并提出关于广义Cauchy张量互补问题的几个结论.     

Positive Definiteness of Complex Piecewise Linear Functions and Some of Its Applications

Mathematical Notes - Given α ∈ (0, 1) and c = h + iβ, h, β ∈ R, the function fα,c: R → C defined as follows is considered: (1) fα,c is Hermitian, i.e.,...     

关于矩阵的泛正定性与广义逆偏序的一个注记

指出“矩阵的泛正定性与广义逆偏序”一文的一些错误,利用矩阵的同时合同变换给出了矩阵偏序的若干刻画.     

Modified subspace limited memory BFGS algorithm for large-scale bound constrained optimization

In this paper, a subspace limited memory BFGS algorithm for solving large-scale bound constrained optimization problems is developed. It is modifications of the subspace limited memory quasi-Newton method proposed by Ni and Yuan [Q. Ni, Y.X. Yuan, A subspace limited memory quasi-Newton algorithm for large-scale nonlinear bound constrained optimization, Math. Comput. 66 (1997) 1509–1520]. An important property of our proposed method is that more limited memory BFGS update is used. Under appropriate conditions, the global convergence of the method is established. The implementations of the method on CUTE test problems are presented, which indicate the modifications are beneficial to the performance of the algorithm.     

Limited-memory BFGS with displacement aggregation

A displacement aggregation strategy is proposed for the curvature pairs stored in a limited-memory BFGS (a.k.a. L-BFGS) method such that the resulting (inverse) Hessian approximations are equal to those that would be derived from a full-memory BFGS method. This means that, if a sufficiently large number of pairs are stored, then an optimization algorithm employing the limited-memory method can achieve the same theoretical convergence properties as when full-memory (inverse) Hessian approximations are stored and employed, such as a local superlinear rate of convergence under assumptions that are common for attaining such guarantees. To the best of our knowledge, this is the first work in which a local superlinear convergence rate guarantee is offered by a quasi-Newton scheme that does not either store all curvature pairs throughout the entire run of the optimization algorithm or store an explicit (inverse) Hessian approximation. Numerical results are presented to show that displacement aggregation within an adaptive L-BFGS scheme can lead to better performance than standard L-BFGS.

     

求解一般约束优化问题的修正BFGS信赖域算法

借鉴无约束优化问题的BFGS信赖域算法,建立了非线性一般约束优化问题的BFGS信赖域算法,并证明了算法的全局收敛性．数值实验表明,算法是有效的．     

一个新的BFGS信赖域算法

本文对于无约束最优化问题提出了一个新的BFGS信赖域算法.利用BFGS方法和信赖域方法,提出了改进的BFGS信赖域方法.推广了文献[3,5]中的两种算法,得到一个新的BFGS信赖域算法,在适当条件下证明了算法的全局收敛性.     

修正的期权定价模型及定价公式

通过一系列函数变换，求出了修正的Black—Scholes欧式定价模型方程的解，并对股票与国债的投资组合进行了分析。