大规模最小二乘奇异值分解的并行处理方法

大规模最小二乘问题求解中,直接进行奇异值分解会产生巨大的内存需求以及漫长的计算时间。为解决该问题,提出了一种基于迭代的并行处理方法。该方法利用奇异值分解降维的特性,通过迭代不断减小矩阵规模,直到可以直接使用奇异值分解求解。在迭代过程中,将矩阵分解为许多足够小的子矩阵,并行处理其奇异值分解过程,从而提升运行速度。实验结果表明,该方法即使是串行处理,也使得大规模最小二乘奇异值分解的时间成本及空间成本大大降低;而并行处理在双机条件下加速比接近200％。     

基于增广矩阵束方法的平面天线阵列综合

针对平面阵列的稀布优化问题,提出了一种基于增广矩阵束方法的减少阵元数目、求解阵元位置和设计幅度激励的优化方法。首先对期望平面阵的方向图进行采样并由采样点数据构造增广矩阵,对此矩阵进行奇异值(SVD)分解,确定在误差允许范围内所需的最小阵元数目;然后基于广义特征值分解分别计算两组特征值,并根据类ESPRIT算法对特征值进行配对;最后在最小二乘准则条件下根据正确的特征值对求解平面阵列的阵元位置和激励。仿真结果表明该算法具有较高的计算效率和数值精度。     

超声逆散射成像方法研究

在研究奇异值分解、最小二乘法的基础上,采用空间域方法研究超声逆散射成像问题。通过脉冲基和点匹配的方法将泛函方程转换为代数方程,运用迭代算法解决方程的非线性问题。利用Picard准则判断方程的不适定程度,并采用均值处理和截断奇异值分解正则化2种方法对方程进行求解。实验结果证明,该方法可以较好地滤除噪声,提高重建图像的质量和可信度,减少迭代过程中的计算量。     

一种求解约束优化问题的混合算法

提出一种基于修改增广Lagrange函数和PSO的混合算法用于求解约束优化问题。将约束优化问题转化为界约束优化问题,混合算法由两层迭代结构组成,在内层迭代中,利用改进PSO算法求解界约束优化问题得到下一个迭代点。外层迭代主要修正Lagrange乘子和罚参数,检查收敛准则是否满足,重构下次迭代的界约束优化子问题,检查收敛准则是否满足。数值实验结果表明该混合算法的有效性。     

雅克比矩阵近似更新的三维装配约束求解研究

提出了三维装配约束求解中雅克比矩阵近似更新的方法。该方法通过对 迭代过程中满秩以及行秩秩亏雅克比矩阵进行近似更新,提高了约束求解的效率。首先在非 线性迭代求解过程中添加雅克比矩阵及其逆矩阵近似更新的公式;然后给出使用近似更新公 式需要满足的限制条件;最后通过对奇异点扰动算法的描述介绍迭代求解过程中雅克比矩阵 发生行秩秩亏的处理办法。文中提出的策略与算法已在三维装配约束求解引擎CBABench 中实现,给出的实例表明本文提出的方法效果显著。     

Kronecker积重构卷积核矩阵的图像迭代复原方法

图像复原实际上是反卷积问题,其中的卷积核矩阵属于大尺寸的Toeplitz矩阵。为了降低迭代复原算法的计算复杂度,通过分析该Toeplitz系统的病态性及常见快速求解方法,提出一种基于卷积核矩阵重构的预条件共轭梯度迭代算法。首先根据Toeplitz矩阵可分解为Kronecker积的和的性质,对点扩散函数进行奇异值分解,将各奇异值对应的左右向量构造子Toeplitz矩阵,子矩阵作Kronecker积并加和,从而得到卷积核矩阵的分解式,然后根据Kronecker乘积的性质,将该分解式用于构造预条件算子,最后利用预条件共轭梯度法求解。计算复杂度分析及实验表明该方法有助于加速迭代的收敛并得到稳定结果。     

快速判定几何约束奇异性的切面扰动法

针对冗余奇异和分支奇异的判定问题,提出一种新的切面扰动的判定方法.该方法将奇异的雅可比矩阵分为独立构型空间和奇异空间,变量沿独立构型空间的切面扰动,计算更新的雅克比矩阵的秩,依据秩亏的变化可以快速、稳定地判定约束奇异性.该算法克服了残量扰动法的数值迭代、计算量大和不稳定的缺点,并且在参数化特征造型系统InteSolid中得到验证.     

单变量矩阵方程子矩阵约束下牛顿-MCG算法

子矩阵约束问题源于实际应用中的子系统扩张问题,文中研究了子矩阵约束下二次矩阵方程对称解的迭代算法,先用牛顿算法把二次矩阵方程转化为关于校正矩阵的线性矩阵方程,再用修正共轭梯度算法(MCG算法)求解导出线性矩阵方程对称解或最小二乘解,建立了求单变量二次矩阵方程子矩阵约束下对称解牛顿-MCG算法.数值算例表明,该牛顿-MCG是有效的,能在有限步迭代得到方程的子矩阵约束解.     

自由漂浮空间机器人回避动力学奇异的轨迹规划

针对非完整约束导致的自由漂浮空间机器人广义雅可比矩阵动力学奇异性问题,提出一种间接求解机器人逆运动学的方案.该方案通过运动方程分解并构造迭代函数,将动力学奇异转换为运动学奇异问题,避免直接求解逆广义雅可比矩阵,解决广义雅可比矩阵动力学参数相关特性问题,仿真结果表明该方法能够有效地实现自由漂浮空间机器人回避动力学奇异的非完整轨迹规划.     

结构光系统的自动标定

提出一种基于单应矩阵的摄像机自动标定算法。讨论摄像机焦距为恒定和任意变化两种情况下求解摄像机内参数的计算方法:论证空间平面诱导单应矩阵的性质,利用该性质不但能求出摄像机外参数,还可得到空间平面法向量和单应矩阵方程的比例因子。该算法在求解过程中不需要非线性迭代,可以直接获得解析解,实验表明该算法具有很好的准确性、普遍性。     

状态饱和离散线性系统的稳定性分析

讨论一类具有状态饱和非线性的离散线性系统稳定性分析问题. 通过引入无穷范数小于等于1 的自由矩阵与对角元素非正的对角矩阵, 将状态饱和离散线性系统的状态变量约束在一个凸多面体内, 进而以矩阵不等式形式给出状态饱和离散线性系统的稳定性判据, 并给出该矩阵不等式的迭代线性矩阵不等式算法. 基于这一稳定性判据, 给出了基于迭代线性矩阵不等式的状态反馈控制律设计算法. 通过状态饱和离散线性系统的状态空间分割方法, 给 出了保守性更小的稳定性判据, 并给出了相应的迭代线性矩阵不等式算法. 数值例子验证了所给出方法的正确性与有效性.

     

Efficient Suboptimal Solutions to the Optimal Triangulation

Given two images, the optimal triangulation of a measured corresponding point pair is to basically find out the real roots of a 6-degree polynomial. Since for each point pair, this root finding process should be done, the optimal triangulation for the whole image is computationally intensive. In this work, via the 3D cone expression of fundamental matrix, called the fundamental cone, together with the Lagrange’s multiplier method, the optimal triangulation problem is reformulated. Under this new formulation, the optimal triangulation for a measured point pair is converted to finding out the closest point on the fundamental cone to the measured point in the joint image space, then 3 efficient suboptimal algorithms, each of them can satisfy strictly the epipolar constraint of the two images, are proposed. In our first suboptimal algorithm, the closest point on the generating cone to the measured point is used as the approximation of the optimal solution, which is to find out the real roots of a 4-degree polynomial; in our second suboptimal algorithm, the closest point on the generating line to the measured point is used as the approximation of the optimal solution, which is to find out the real roots of a 2-degree polynomial. Finally, in our third suboptimal algorithm, the converging point of the Sampson approximation sequence is used as the approximation of the optimal solution. Experiments with simulated data as well as real images show that our proposed 3 suboptimal algorithms can achieve comparable estimation accuracy compared with the original optimal triangulation, but with much less computational load. For example, our second and third suboptimal algorithms take only about a 1/5 runtime of the original optimal solution. Besides, under our new formulation, rather than recompute the two Euclidian transformation matrices for each measured point pair, a fixed Euclidian transformation matrix is used for all image point pairs, which, in addition to its mathematical elegance and computational efficiency, is able to remove the dependency of the resulting polynomial’s degree on the parameterization of the epipolar pencil in either the first image or in the second image, a drawback in the original optimal triangulation.     

Suboptimal Solutions to the Algebraic-Error Line Triangulation

Line triangulation, a foundational problem in computer vision, is to estimate the 3D line position from a set of measured image lines with known camera projection matrices. Aiming to improve the triangulation’s efficiency, in this work, two algorithms are proposed to find suboptimal solutions under the algebraic-error optimality criterion of the Plücker line coordinates. In these proposed algorithms, the algebraic-error optimality criterion is reformulated by the transformation of the Klein constraint. By relaxing the quadratic unit norm constraint to six linear constraints, six new single-quadric-constraint optimality criteria are constructed in the new formulation, whose optimal solutions can be obtained by solving polynomial equations. Moreover, we prove that the minimum algebraic error of either the first three or the last three of the six new criteria is not more than \(\sqrt{3}\) times of that of the original algebraic-error optimality criterion. Thus, with three new criteria and all the six criteria, suboptimal solutions under the algebraic error minimization and the geometric error minimization are obtained. Experimental results show the effectiveness of our proposed algorithms.     

基于改进PSO-TrICP算法的点云配准

针对传统迭代最近点（Iterative Closest Point,ICP）算法在初始空间位置偏差大时,容易陷入局部最优的问题,提出一种基于改进PSO-TrICP算法的点云配准方法。首先,对传统粒子群（Particle Swarm Optimization,PSO）算法进行改进,引入适应度的相似度测量准则调整粒子的更新方式,然后加入历次迭代的全局最优解的均值作为新的学习因子避免求解过程中出现“早熟”现象;其次用刚性变换参数和点云间的重叠率组成粒子,利用改进PSO算法为配准提供良好的初始相对位置;最后,通过裁剪迭代最近点（Trimmed Iterative Closest Point,TrICP）算法估计点云间的空间变换。实验结果表明,改进PSO-TrICP算法的配准精度与运行效率优于近年提出的同类配准算法,且具有较好的鲁棒性。     

Optimality and stability of the K-hyperline clustering algorithm

K-hyperline clustering is an iterative algorithm based on singular value decomposition and it has been successfully used in sparse component analysis. In this paper, we prove that the algorithm converges to a locally optimal solution for a given set of training data, based on Lloyd’s optimality conditions. Furthermore, the local optimality is shown by developing an Expectation-Maximization procedure for learning dictionaries to be used in sparse representations and by deriving the clustering algorithm as its special case. The cluster centroids obtained from the algorithm are proved to tessellate the space into convex Voronoi regions. The stability of clustering is shown by posing the problem as an empirical risk minimization procedure over a function class. It is proved that, under certain conditions, the cluster centroids learned from two sets of i.i.d. training samples drawn from the same probability space become arbitrarily close to each other, as the number of training samples increase asymptotically.     

Point cloud matching using singular value decomposition

We propose a simple, fast three-dimensional (3D) matching method that determines the best rotation matrix between non-corresponding point clouds (PCs) with no iterations. An estimated rotation matrix can be derived by the two following steps: (1) the singular value decomposition is applied to a measured data matrix, and a database matrix is constructed from the PC datasets; (2) the inner product of each left singular vector is used to produce the estimated rotation. Through experimentation, we demonstrate that the proposed method executes 3D PC matching with <4 % of the computational time of the iterative closest point algorithm with nearly identical accuracy.     

Estimating the fundamental matrix via constrained least-squares: aconvex approach

In this paper, a new method for the estimation of the fundamental matrix from point correspondences in stereo vision is presented. The minimization of the algebraic error is performed while taking explicitly into account the rank-two constraint on the fundamental matrix. It is shown how this nonconvex optimization problem can be solved avoiding local minima by using recently developed convexification techniques. The obtained estimate of the fundamental matrix turns out to be more accurate than the one provided by the linear criterion, where the rank constraint of the matrix is imposed after its computation by setting the smallest singular value to zero. This suggests that the proposed estimate can be used to initialize nonlinear criteria, such as the distance to epipolar lines and the gradient criterion, in order to obtain a more accurate estimate of the fundamental matrix     

Stability analysis for discrete linear systems with state saturation by a saturation-dependent Lyapunov functional

This paper concerns the stability analysis problem of discrete linear systems with state saturation using a saturation-dependent Lyapunov functional.We introduce a free matrix characterized by the sum of the absolute value of each elements for each row less than 1,which makes the state with saturation constraint reside in a convex polyhedron.A saturation-dependent Lyapunov functional is then designed to obtain a sufficient condition for such systems to be globally asymptotically stable.Based on this stability criterion,the state feedback control law synthesis problem is also studied.The obtained results are formulated in terms of bilinear matrix inequalities that can be solved by the presented iterative linear matrix inequality algorithm.Two numerical examples are used to demonstrate the effectiveness of the proposed method.     

带特征线约束的Delaunay三角剖分最优算法的研究及实现

为了提高特征线约束的Delaunay三角剖分的速度和功率,从两个方面进行改进;一是生成无约束的Delaunay三角网时,采用进行剖分算法;二是在约束线上插入点时,应用取三角形外接圆与特征线交点的方法。并行剖分算法具有较好的加速性能;“交点”插入算法考虑了特征线的影响域及Delaunay三角形规则的边界条件,在满足全局Delaunay三角剖分的前提下,使插入的点最少,对原有的网格影响最小。