A complete algebraic solvability test for the nonstrict Lyapunov inequality

For arbitrary equally sized square complex matrices A and Q (Q Hermitian), the paper provides a complete algebraic test for verifying the existence of a Hermitian solution X of the nonstrict Lyapunov inequality A^*X + XA + Q 0. If existing, we exhibit how to construct a solution. Our approach involves the validation problem for the linear matrix inequality Σ_j=1^k (A_j^*X_jB_j + B_j^*X_j^*A_j) + Q> 0 in X_j, for which we provide an algebraic solvability test and a construct solutions if the kernels of A_j or, dually, those of B_j form an isotonic sequence.     

Spacetime-minimal systolic arrays for Gaussian elimination and the Algebraic path problem

In this paper, we derive time-minimal systolic arrays for Gaussian elimination and the Algebraic Path Problem (APP) that use a minimal number of processors. For a problem of size n, we obtain an execution time T(n) = 3n −1 using A(n) = n²/4+O(n) processors for Gaussian elimination, and T(n) = 5n −2 and A(n) = n³/+O(n) for the APP.     

Efficient enumeration of all minimal separators in a graph

This paper presents an efficient algorithm for enumerating all minimal a-b separators separating given non-adjacent vertices a and b in an undirected connected simple graph G = (V, E), Our algorithm requires O(n³R_ab) time, which improves the known result of O(n⁴R_ab) time for solving this problem, where ¦V¦= n and R_ab is the number of minimal a-b separators. The algorithm can be generalized for enumerating all minimal A-B separators that separate non-adjacent vertex sets A, B < V, and it requires O(n²(n − n_A − n_b)R_AB) time in this case, where n_a = ¦A¦, n_B = ¦B¦ and r_AB is the number of all minimal A−B separators. Using the algorithm above as a routine, an efficient algorithm for enumerating all minimal separators of G separating G into at least two connected components is constructed. The algorithm runs in time O(n³R⁺_Σ + n⁴R_Σ), which improves the known result of O(n⁶R_Σ) time, where R_σ is the number of all minimal separators of G and R_ΣR⁺_Σ = ∑_1i, v_j) ER_vivj n − 1)/2 − m)R_Σ. Efficient parallelization of these algorithms is also discussed. It is shown that the first algorithm requires at most O((n/log n)R_ab) time and the second one runs in time O((n/log n)R⁺_Σ+n log nR_Σ) on a CREW PRAM with O(n³) processors.     

Computerized formal solutions of dynamical systems with two degrees of freedom and an application to the Contopoulos potential—II : The near-resonance case

The article describes the periodic solutions of the Contopoulos system for the case of near-resonant frequencies (ω₂² = 1 and ω = 1 − ε²² The Lindstedt method is used throughout with all the literal algebraic manipulations being computerized so that all expansions are carried to the fourth order in the small parameter ε.

It is shown that each of the two normal modes of oscillation has a bifurcation (really trifurcation) point which moves towards the origin when the exact resonance is approached, explaining why the one-to-one resonant Contopoulos system has six modes of periodic oscillations near the origin, rather then the usual number of two.

We give a single Lindstedt-type literal expansion which is valid for the three intersecting families of periodic solutions. This expansion contains two constants, A and D, representing the direct and retrograde circulations C⁺ and C⁻ when both constants are non-zero and the vertical normal mode family when A = 0.

The verifications of the analytical results by numerical integrations are also given.     

Constant-time algorithm for computing the Euclidean distance maps of binary images on 2D meshes with reconfigurable buses

The computation of Euclidean distance maps (EDM), also called Euclidean distance transform, is a basic operation in computer vision, pattern recognition, and robotics. Fast computation of the EDM is needed since most of the applications using the EDM require real-time computation. It is shown in L. Chen and H.Y.H. Chuang [Information Processing Letters, 51, pp. 25–29 (1994)] that a lower bound Ω(n²) is required for any sequential EDM algorithm due to the fact that in any EDM algorithm each of the n² pixels has to be scanned at least once. Recently, many parallel EDM algorithms have been proposed to speedup its computation. Chen and Chuang proposed an algorithm for computing the EDM on an n×n mesh in O(n) time [L. Chen and H.Y.H. Chuang Parallel Computing, 21, pp. 841–852 (1995)]. Clearly, the VLSI complexities of both the sequential and the mesh algorithm described in L. Chen and H.Y.H. Chuang [Parallel Computing, 21, pp. 841–852 (1995)] are AT²=O(n⁴), where A is the VLSI layout area of the design and T is the computation time using area A when implemented in VLSI. In this paper, we propose a new and faster parallel algorithm for computing the EDM problem on the reconfigurable VLSI mesh model. For the same problem, our algorithm runs in O(1) time on a two-dimensional n²×n² reconfigurable mesh. We show that the VLSI complexity of our algorithm is the same as those of the above sequential algorithm and the mesh algorithm, while it uses much less time. To our best knowledge, this is the first constant-time EDM algorithm on any parallel computational model.     

Analytic numerical solution of coupled semi-infinite diffusion problems

In this paper, we consider coupled semi-infinite diffusion problems of the form u_t(x, t)− A² u_xx(x,t) = 0, x> 0, t> 0, subject to u(0,t)=B and u(x,0)=0, where A is a matrix in , and u(x,t), and B are vectors in . Using the Fourier sine transform, an explicit exact solution of the problem is proposed. Given an admissible error and a domain D(x₀,t₀)={(x,t);0≤x≤x₀, t≥t₀ > 0, an analytic approximate solution is constructed so that the error with respect to the exact solution is uniformly upper bounded by in D(x₀, t₀).     

An approximate A algorithm and its application to the SCS problem

In this paper we deal with algorithm A^* and its application to the problem of finding the shortest common supersequence of a set of sequences. A^* is a powerful search algorithm which may be used to carry out concurrently the construction of a network and the solution of a shortest path problem on it. We prove a general approximation property of A^* which, by building a smaller network, allows us to find a solution with a given approximation ratio. This is particularly useful when dealing with large instances of some problem. We apply this approach to the solution of the shortest common supersequence problem and show its effectiveness.     

Linear rotation based algorithm and systolic architecture for solving linear system equations

A linear rotation based algorithm is proposed for solving linear system equations, Ax = b. This algorithm modified the conventional Gaussian elimination method and can avoid the problems of numerical singularity and ill condition. In this study, the implementation of a trapezoidal systolic array of n²/2 + n −2 processors as well as a linear array of n processors are accomplished for this algorithm. The trapezoidal systolic array performs the triangularization of a matrix A by using the modified linear rotation algorithm; while the linear array performs the backward substitution for evaluating the solution of x. The computing time for solving a linear equation system will be O(5n) time units. Also an implicit representation of the elimination factor by means of the sign parameter sequence instead of an numerical value is introduced for simplifying the hardware complexity. It is clear that this systolic architecture is simple, uniform, and regular, and therefore well suitable for the implementation of a VLSI chip.     

Constructing space-filling curves of compact connected manifolds

Let M be a compact connected (topological) manifold of finite- or infinite-dimension n. Let 0 r 1 be arbitrary but fixed. We construct in this paper a space-filling curve f from [0,1] onto M, under which M is the image of a compact set A of Hausdorff dimension r. Moreover, the restriction of f to A is one-to-one over the image of a dense subset provided that 0 r log|2ⁿ/log(2ⁿ + 2). The proof is based on the special case where M is the Hilbert cube [0,1]^ω.     

On the sensitivity of camera calibration

The orientation position errors of an object's coordinate frame are determined when the offset of image centre and lens distortion are not included in the calibration process. The orientation and position errors are [(u₀)² + (v₀)²]^0.5/f and [(u²₀+v²₀)T²_z + (u²₀T²_z + v²₀T_y²)]^0.5/f, respectively, where f is the focal length, (u₀, v₀) is the offset of image centre and (T_x T_y T_z) is the position of an object. We also obtain the following conclusions: (a) The offset of image centre has little effect on the determinations of the position and orientation of a coordinate frame; (b) the lens distortion will not dramatically change the position and orientation of a coordinate frame; (c) the scale factor has a great effect on the position of a coordinate frame, and on the accuracy of measurement; (d) the offset of image centre is more sensitive than the lens distortion on the determinations of the position and orientation of a coordinate frame. Finally, some experimental results are given to demonstrate the theoretical analysis given in this paper.     

Parallel nested dissection

Nested dissection is a very popular direct method for solving sparse linear systems that arise from finite difference and finite element methods. Worley and Schreiber [16] give a fine grain algorithm for a square array of processors. Their algorithm uses O(N²) processors, each with O(N) memory, to factor an N² by N² sparse matrix whose graphs is an N × N mesh. The efficiency of their method is between 1/46 and 1/12. George et al. [6] [8] give a medium grain algorithm for hypercube architecture, while George et al. [7] give an algorithm for shared memory machines. These papers present a column oriented approach which can exploit O(N) parallelism and yield efficiencies up to 50%. Lucas [11] also gives a column oriented scheme which achieves up to 75% efficiency and O(N) parallelism. In this paper, we present a medium to fine grain algorithm for a P × P array of processors with local memory. This algorithm can exploit up to O(N²) parallelism. The efficiency of the fine grain version is comparable to [16] while as a medium grain algorithm achieves about 49% efficiency. The strength of the method is due to three factors: its ability to pipeline much of the computation, overlapping computation and communication, and the use of level 3 BLAS like primitives. In addition to its high efficiency its memory requirement is optimal, only O(N² log N/P²) words memory is needed per processor.     

Lax-stability of fully discrete spectral methods via stability regions and pseudo-eigenvalues

In many calculations, spectral discretization in space is coupled with a standard ordinary differential equation formula in time. To analyze the stability of such a combination, one would like simply to test whether the eigenvalues of the spatial discretization operator (appropriately scaled by the time step k) lie in the stability region for the o.d.e. formula, but it is well known that this kind of analysis is in general invalid. In the present paper we rehabilitate the use of stability regions by proving that a discrete linear multistep ‘method of lines’ approximation to a partial differential equation is Lax-stable, within a small algebraic factor, if and only if all of the -pseudo-eigenvalues of the spatial discretization operator lie within O() of the stability region as → 0. An -pseudo-eigenvalue of a matrix A is any number that is an eigenvalue of some matrix A + E with E ; our arguments make use of resolvents and are closely related to the Kreiss matrix theorem. As an application of our general result, we show that an explicit N-point Chebyshev collocation approximation of u_t = −xu_x on [−1, 1] is Lax-stable if and only if the time step satisfies k = O(N⁻²), although eigenvalue analysis would suggest a much weaker restriction of the form k CN⁻¹.     

An improved parallel Jacobi method for diagonalizing a symmetric matrix

We compare five implementations of the Jacobi method for diagonalizing a symmetric matrix. Two of these, the classical Jacobi and sequential sweep Jacobi, have been used on sequential processors. The third method, the parallel sweep Jacobi, has been proposed as the method of choice for parallel processors. The fourth and fifth methods are believed to be new. They are similar to the parallel sweep method but use different schemes for selecting the rotations.

The classical Jacobi method is known to take O(n⁴) time to diagonalize a matrix of order n. We find that the parallel sweep Jacobi run on one processor is about as fast as the sequential sweep Jacobi. Both of these methods take O(n³ log₂n) time. One of our new methods also takes O(n³ log₂n) time, but the other one takes only O(n³) time. The choice among the methods for parallel processors depends on the degree of parallelism possible in the hardware. The time required to diagonalize a matrix on a variety of architectures is modeled.

Unfortunately for proponents of the Jacobi method, we find that the sequential QR method is always faster than the Jacobi method. The QR method is faster even for matrices that are nearly diagonal. If we perform the reduction to tridiagonal form in parallel, the QR method will be faster even on highly parallel systems.     

A parallel Householder tridiagonalization stratagem using scattered square decomposition

The parallel stratagem in this paper uses scattered square decomposition, introduced by G. Fox, for its data assignment and then exploits parallelism in the solution steps of the sequential Householder tridiagonalization algorithm. One may condense a real symmetric full matrix A of order n into a tridiagonal form by the stratagem in concurrent machines where N(= D²) processors are used. Expressions for efficiency and speedup are given for the evaluation of the stratagem. An alternative stratagem which requires less data transmission but more computations is also discussed. The results shown that the Householder Method of tridiagonalization may be implemented on a concurrent machine efficiently by scattered square decomposition provided that the number of matrix elements contained in each processor is much larger than the number of processors of the concurrent machine, and the ratio of the time to transmit one data item from one processor to any other processor to the time to perform a floating-point arithmetic operation is small enough.     

The limitedness problem on distance automata: Hashiguchi''s method revisited

Hashiguchi has studied the limitedness problem of distance automata (DA) in a series of paper [(J. Comput System Sci. 24 (1982) 233; Theoret. Comput. Sci. 72 (1990) 27; Theoret. Comput. Sci. 233 (2000) 19)]. The distance of a DA can be limited or unbounded. Given that the distance of a DA is limited, Hashiguchi has proved in Hashiguchi (2000) that the distance of the automaton is bounded by 2^{4n3+nlg(n+2)+n}, where n is the number of states. In this paper, we study again Hashiguchi's solution to the limitedness problem. We have made a number of simplification and improvement on Hashiguchi's method. We are able to improve the upper bound to 2^{3n3+nlgn+n−1}.     

Fast evaluation of radial basis functions: I

This paper describes some new techniques for the rapid evaluation and fitting of radial basic functions. The techniques are based on the hierarchical and multipole expansions recently introduced by several authors for the calculation of many-body potentials. Consider in particular the N term thin-plate spline, s(x) = Σ_j=1^N d_jφ(x−x_j), where φ(u) = |u|²log|u|, in 2-dimensions. The direct evaluation of s at a single extra point requires an extra O(N) operations. This paper shows that, with judicious use of series expansions, the incremental cost of evaluating s(x) to within precision ε, can be cut to O(1+|log ε|) operations. In particular, if A is the interpolation matrix, a_i,j = φ(x_i−x_j, the technique allows computation of the matrix-vector product Ad in O(N), rather than the previously required O(N²) operations, and using only O(N) storage. Fast, storage-efficient, computation of this matrix-vector product makes pre-conditioned conjugate-gradient methods very attractive as solvers of the interpolation equations, Ad = y, when N is large.     

An augmented Voronoi roadmap for 3D translational motion planning for a convex polyhedron moving amidst convex polyhedral obstacles

This paper concerns the development of a piecewise linear Voronoi roadmap for translating a convex polyhedron in a three-dimensional (3-D) polyhedral world. In general the Voronoi roadmap is incomplete for motion planning, i.e., it can have several disjoint components in one connected component of free space. An analysis of the roadmap shows that incompleteness is caused by the occurrence of the following simple geometric structure: a polygon in the Voronoi surface containing one or more polygons inside it. We formally bring out the details of this geometric structure and give an efficient augmentation of the roadmap that makes it complete. We show that the roadmap has size e = O(n²Q²l²), where n is the total number of faces on the obstacles, Q is the total number of obstacles and l is the number of faces on the moving object. We also present an algorithm to construct the roadmap in O((n + Ql)e + Q²log Q) time.     

Efficient algorithms for computing two nearest-neighbor problems on a rap

This paper makes an improvement of computing two nearest-neighbor problems of images on a reconfigurable array of processors (RAP) by increasing the bus width between processors. Based on a base-n system, a constant time algorithm is first presented for computing the maximum/minimum of N log N-bit unsigned integers on a RAP using N processors each with N^1/c-bit bus width, where c is a constant and c ≥ 1. Then, two basic operations such as image component labeling and border following are also derived from it. Finally, these algorithms are used to design two constant time algorithms for the nearest neighbor black pixel and the nearest neighbor component problems on an N^1/2 × N^1/2 image using N^1/2 × N^1/2 processors each with N^1/c-bit bus width, where c is a constant and c ≥ 1. Another contribution of this paper is that the execution time of the proposed algorithms is tunable by the bus width.     

Algorithms of discretization of algebraic spatial curves on homogeneous cubical grids

In this paper new methods of discretization (integer approximation) of algebraic spatial curves in the form of intersecting surfaces P(x, y, z) = 0 and Q(x, y, z) = 0 are analyzed.

The use of homogeneous cubical grids G(h³) to discretize a curve is the essence of the method. Two new algorithms of discretization (on 6-connected grid G_6c(h³) and 26-connected grid G₂₆(h³)) are presented based on the method above. Implementation of the algorithms for algebraic spatial curves is suggested. The elaborated algorithms are adjusted for application in computer graphics and numerical control of machine tools.     

McClellan transformation and the construction of biorthogonal wavelet bases of LR

Bidimensional wavelet bases are constructed by means of McClellan's transformation applied to a pair of one-dimensional biorthogonal wavelet filters. It is shown that under some conditions on the transfer function F(ω₁,ω₂) associated to the McClellan transformation and on the dilation matrix D, it is possible to construct symmetric compactly supported biorthogonal wavelet bases of L²(R²). Finally, the construction method is illustrated by means of numerical examples.