由投影重建图象的样条拟合法

曾获得诺贝尔医学奖的CT(计算机断层扫描)技术,其应用范围已从医学扩展到地质勘探、工业无损探伤等许多工程和科技领域.它的理论基础就是由投影重建图象这样一个数学问题,即已知某函数(在屏幕上表现为图象)沿低维流形(二维情形为直线或曲线,三维情形为平面或曲面)的积分值,反求函数(图象)本身.拉当早在1 916年就提出并研究了这一问题,一般称为拉当变换及其反演.目前已有很多实用算法,如代数重建法,卷积反投影法等田.而CT技术应用范围不断扩大,又促进了算法的研究     

A Rational Basis for Function Approximation

A method is developed for constructing a convenient basis ofrational wedge functions for collocation on convex polygon boundaries.     

A Gradient-Descent Method for Curve Fitting on Riemannian Manifolds

Given data points p ₀,…,p _N on a closed submanifold M of ℝ ⁿ and time instants 0=t ₀<t ₁<⋅⋅⋅<t _N =1, we consider the problem of finding a curve γ on M that best approximates the data points at the given instants while being as “regular” as possible. Specifically, γ is expressed as the curve that minimizes the weighted sum of a sum-of-squares term penalizing the lack of fitting to the data points and a regularity term defined, in the first case as the mean squared velocity of the curve, and in the second case as the mean squared acceleration of the curve. In both cases, the optimization task is carried out by means of a steepest-descent algorithm on a set of curves on M. The steepest-descent direction, defined in the sense of the first-order and second-order Palais metric, respectively, is shown to admit analytical expressions involving parallel transport and covariant integral along curves. Illustrations are given in ℝ ⁿ and on the unit sphere.     

求解一般约束优化问题的一个全局收敛的混合不精确SQP算法

对于一般约束优化问题,本文通过一种特殊的耦合策略,把一个局邵超线性收敛的不精确SQP算法与广义梯度投影法相结合,从而给出了一个混合算法.该算法无需计算拉格朗日函数的海色矩阵,并且在适当的假设下,算法具有全局和局部超线性收敛性.     

一种收入分配洛伦兹曲线拟合的线性模型研究

洛伦兹曲线是用来描述社会收入分配状况的一种曲线,能够精确有效地拟合洛伦兹曲线是进行收入分配定量分析的基础.基于洛伦兹曲线的经济学规律和数学特点,提出了一种新的指数函数多项式拟合洛伦兹曲线的线性模型,并讨论研究模型的矩阵广义逆和线性最小二乘求解方法.通过与5个典型的非线性拟合模型的实例对比分析,验证了方法具有收敛稳定、精度高、对噪声不敏感的优点.     

A General Multiresolution Method for Fitting Functions on the Sphere

In [7], Lyche and Schumaker have described a method for fitting functions of class C ¹ on the sphere which is based on tensor products of quadratic polynomial splines and trigonometric splines of order three associated with uniform knots. In this paper, we present a multiresolution method leading to C ²-functions on the sphere, using tensor products of polynomial and trigonometric splines of odd order with arbitrary simple knot sequences. We determine the decomposition and reconstruction matrices corresponding to the polynomial and trigonometric spline spaces. We describe the general tensor product decomposition and reconstruction algorithms in matrix form which are convenient for the compression of surfaces. We give the different steps of the computer implementation of these algorithms and, finally, we present a test example.     

T样条函数空间的局部支集基

Ｓｃｈｕｍａｋｅｒ，Ｌ．Ｌ．在其名著《ＳｐｌｉｎｅＦｕｎｃｔｉｏｎ：ＢａｓｉｃＴｈｅｏｒｙ》中第九章给出了Ｔｃｈｅｂｙｓｈ－ｅｆ样条函数空间的局部支集基定理，可惜其证明却是错的，本文给出了上述定理的正确证明．     

求解无约束优化问题的一个秩一适定方法

在本文中,我们给出一个求解无约束优化问题的秩一适定方法,该方法具有下述较好性质:校正矩阵是对称正定的;在适当条件下,对非凸函数拥有全局收敛性.我们还给出数值检验结果.     

A General Framework for Mining Massive Data Streams

In many domains, data now arrive faster than we are able to mine it. To avoid wasting these data, we must switch from the traditional “one-shot” data mining approach to systems that are able to mine continuous, high-volume, open-ended data streams as they arrive. In this article we identify some desiderata for such systems, and outline our framework for realizing them. A key property of our approach is that it minimizes the time required to build a model on a stream while guaranteeing (as long as the data are iid) that the model learned is effectively indistinguishable from the one that would be obtained using infinite data. Using this framework, we have successfully adapted several learning algorithms to massive data streams, including decision tree induction, Bayesian network learning, k-means clustering, and the EM algorithm for mixtures of Gaussians. These algorithms are able to process on the order of billions of examples per day using off-the-shelf hardware. Building on this, we are currently developing software primitives for scaling arbitrary learning algorithms to massive data streams with minimal effort.     

一般平面曲线流的一点注记

讨论嵌入平面闭曲线的一般发展方程,并给出发展曲线的各种几何量的演化方程,然后证明发展方程的切向分量并不影响发展曲线的最终形状。     

Use of Residuals in Forsythe's Method for Polynomial Curve Fitting

Orthogonal polynomials are widely used in discrete least squarespolynomial curve fitting. Here we explain why a variant proposedrecently by several authors is superior and propose a logicalextension which produces better results under some circumstances.     

A Stochastic Trust-Region Framework for Policy Optimization

In this paper, we study a few challenging theoretical and numerical issues on the well known trust region policy optimization for deep reinforcement learning. The goal is to find a policy that maximizes the total expected reward when the agent acts according to the policy. The trust region subproblem is constructed with a surrogate function coherent to the total expected reward and a general distance constraint around the latest policy. We solve the subproblem using a reconditioned stochastic gradient method with a line search scheme to ensure that each step promotes the model function and stays in the trust region. To overcome the bias caused by sampling to the function estimations under the random settings, we add the empirical standard deviation of the total expected reward to the predicted increase in a ratio in order to update the trust region radius and decide whether the trial point is accepted. Moreover, for a Gaussian policy which is commonly used for continuous action space, the maximization with respect to the mean and covariance is performed separately to control the entropy loss. Our theoretical analysis shows that the deterministic version of the proposed algorithm tends to generate a monotonic improvement of the total expected reward and the global convergence is guaranteed under moderate assumptions. Comparisons with the state-of-the-art methods demonstrate the effectiveness and robustness of our method over robotic controls and game playings from OpenAI Gym.     

A Rational Basis for Function Approximation: II. Curved Sides

Geometric arguments provide a basis for piecewise rational approximationover collections of regions of the type used in finite-elementstudies. The approximation is continous over the collectionof regions and the rational basis functions provide a basisfor linear functions over each element. Conic sides are analysed,and an alternative to isoparametric co-ordinates is described.     

A Radial Basis Function Method for Global Optimization

We introduce a method that aims to find the global minimum of a continuous nonconvex function on a compact subset of . It is assumed that function evaluations are expensive and that no additional information is available. Radial basis function interpolation is used to define a utility function. The maximizer of this function is the next point where the objective function is evaluated. We show that, for most types of radial basis functions that are considered in this paper, convergence can be achieved without further assumptions on the objective function. Besides, it turns out that our method is closely related to a statistical global optimization method, the P-algorithm. A general framework for both methods is presented. Finally, a few numerical examples show that on the set of Dixon-Szegö test functions our method yields favourable results in comparison to other global optimization methods.     

A Hybrid Descent Method for Global Optimization

In this paper, a hybrid descent method, consisting of a simulated annealing algorithm and a gradient-based method, is proposed. The simulated annealing algorithm is used to locate descent points for previously converged local minima. The combined method has the descent property and the convergence is monotonic. To demonstrate the effectiveness of the proposed hybrid descent method, several multi-dimensional non-convex optimization problems are solved. Numerical examples show that global minimum can be sought via this hybrid descent method.     

面向多目标优化的一种混合进化算法

针对多目标优化问题,设计一种基于量子计算和非支配排序遗传算法相结合的智能算法进行求解,综合量子算法和非支配排序遗传算法的优点,在局部搜索和全局搜索之间进行权衡。混合算法采用量子比特对问题的解进行编码,基于量子旋转门算子、分散交叉算子以及高斯变异算子对种群进行更新。进行局部深入搜索时,用一个解在目标空间中跟理想点的距离来评价该解的优劣;进行全局搜索时,基于非支配排序遗传算法中的有效前沿的划分和解之间的拥挤距离来评价某个解。最后,在经典的测试函数ZDT5上对所提混合算法进行了测试。通过对比分析若干项针对有效解集的评价指标,该混合算法在跟最优有效前沿的逼近程度以及有效解集分布的均匀程度上均优于目前得到广泛应用的非支配排序遗传算法。     

A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS

Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of solutions, called the Pareto-optimal front. Thus, the goal of multi-objective strategies is to generate a set of non-dominated solutions as an approximation to this front. However, the majority of problems of this kind cannot be solved exactly because they have very large and highly complex search spaces. In recent years, meta-heuristics have become important tools for solving multi-objective problems encountered in industry as well as in the theoretical field. This paper presents a novel approach based on hybridizing Simulated Annealing and Tabu Search. Experiments on the Graph Partitioning Problem show that this new method is a better tool for approximating the efficient set than other strategies also based on these meta-heuristics.     

A General Global Optimization Approach for Solving Location Problems in the Plane

We propose a general approach for constructing bounds required for the “Big Triangle Small Triangle” (BTST) method for the solution of planar location problems. Optimization problems, which constitute a sum of individual functions, each a function of the Euclidean distance to a demand point, are analyzed and solved. These bounds are based on expressing each of the individual functions in the sum as a difference between two convex functions of the distance, which is not the same as convex functions of the location. Computational experiments with nine different location problems demonstrated the effectiveness of the proposed procedure.     

A Hybrid Particle Swarm Optimization Algorithm for the Redundancy Allocation Problem

The Redundancy Allocation Problem generally involves the selection of components with multiple choices and redundancy levels that produce maximum system reliability given various system level constraints as cost and weight. In this paper we investigate the series–parallel redundant reliability problems, when a mixing of components was considered. In this type of problem both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximise the reliability of system. A hybrid algorithm is based on particle swarm optimization and local search algorithm. In addition, we propose an adaptive penalty function which encourages our algorithm to explore within the feasible region and near feasible region, and discourage search beyond that threshold. The effectiveness of our proposed hybrid PSO algorithm is proved on numerous variations of three different problems and compared to Tabu Search and Multiple Weighted Objectives solutions.