Discrete fourier transform computation using prime Ramanujan numbers

Ramanujan numbers were introduced in [2] to implement discrete fourier transform (DFT) without using any multiplication operation. Ramanujan numbers are related to π and integers which are powers of 2. If the transform sizeN, is a Ramanujan number, then the computational complexity of the algorithms used for computing isO(N ²) addition and shift operations, and no multiplications. In these algorithms, the transform can be computed sequentially with a single adder inO(N ²) addition times. Parallel implementation of the algorithm can be executed inO(N) addition times, withO(N) number of adders. Some of these Ramanujan numbers of order-2 are related to the Biblical and Babylonian values of π [1]. In this paper, we analytically obtain upper bounds on the degree of approximation in the computation of DFT if JV is a prime Ramanujan number.     

Asymptotically fast solution of toeplitz and related systems of linear equations

We present an inversion algorithm for the solution of a generic N X N Toeplitz system of linear equations with computational complexity O(Nlog²N) and storage requirements O(N). The algorithm relies upon the known structure of Toeplitz matrices and their inverses and achieves speed through a doubling method. All the results are derived and stated in terms of the recent concept of displacement rank, and this is used to extend the scope of the algorithm to include a wider class of matrices than just Toeplitz and also to include block Toeplitz matrices.     

A locally optimal fast obstacle-avoiding path-planning algorithm

Two algorithms to compute the shortest collision-free paths in the Euclidean plane are presented. The ƒ obstacles assumed to be described by disjoint convex polygons having N vertices in total. After preprocessing time O(N+flogN), a suboptimal shortest path between two arbitrary query points can be found in O(f+NlogN) time using Dijkstra's algorithm and in Θ(N) time using the A¹ algorithm. The space complexity is O(N+f).     

Numerical solution to generalized Lyapunov/Stein and rational Riccati equations in stochastic control

We consider the numerical solution of the generalized Lyapunov and Stein equations in \(\mathbb {R}^{n}\), arising respectively from stochastic optimal control in continuous- and discrete-time. Generalizing the Smith method, our algorithms converge quadratically and have an O(n³) computational complexity per iteration and an O(n²) memory requirement. For large-scale problems, when the relevant matrix operators are “sparse”, our algorithm for generalized Stein (or Lyapunov) equations may achieve the complexity and memory requirement of O(n) (or similar to that of the solution of the linear systems associated with the sparse matrix operators). These efficient algorithms can be applied to Newton’s method for the solution of the rational Riccati equations. This contrasts favourably with the naive Newton algorithms of O(n⁶) complexity or the slower modified Newton’s methods of O(n³) complexity. The convergence and error analysis will be considered and numerical examples provided.     

An efficient implementation of the robust tabu search heuristic for sparse quadratic assignment problems

We propose and develop an efficient implementation of the robust tabu search heuristic for sparse quadratic assignment problems. The traditional implementation of the heuristic applicable to all quadratic assignment problems is of O(N²) complexity per iteration for problems of size N. Using multiple priority queues to determine the next best move instead of scanning all possible moves, and using adjacency lists to minimize the operations needed to determine the cost of moves, we reduce the asymptotic (N → ∞) complexity per iteration to O(N log N). For practical sized problems, the complexity is O(N).     

An O(N) algorithm for constructing the solution operator to 2D elliptic boundary value problems in the absence of body loads

The large sparse linear systems arising from the finite element or finite difference discretization of elliptic PDEs can be solved directly via, e.g., nested dissection or multifrontal methods. Such techniques reorder the nodes in the grid to reduce the asymptotic complexity of Gaussian elimination from O(N ²) to O(N ^1.5) for typical problems in two dimensions. It has recently been demonstrated that the complexity can be further reduced to O(N) by exploiting structure in the dense matrices that arise in such computations (using, e.g., \(\mathcal {H}\) -matrix arithmetic). This paper demonstrates that such accelerated nested dissection techniques become particularly effective for boundary value problems without body loads when the solution is sought for several different sets of boundary data, and the solution is required only near the boundary (as happens, e.g., in the computational modeling of scattering problems, or in engineering design of linearly elastic solids). In this case, a modified version of the accelerated nested dissection scheme can execute any solve beyond the first in O(N _boundary) operations, where N _boundary denotes the number of points on the boundary. Typically, N _boundary ～ N ^0.5. Numerical examples demonstrate the effectiveness of the procedure for a broad range of elliptic PDEs that includes both the Laplace and Helmholtz equations.     

Fast directional algorithms for the Helmholtz kernel

This paper presents a new directional multilevel algorithm for solving N-body or N-point problems with highly oscillatory kernels. We address the problem by first proving that the interaction between a ball of radius r and a well-separated region has an approximate low rank representation, as long as the well-separated region belongs to a cone with a spanning angle of O(1/r) and is at a distance which is at least O(r²) away from the ball. Based on this representation, our algorithm organizes the high frequency computation using a multidirectional and multiscale strategy. Our algorithm is proved to have an optimal O(NlogN) computational complexity for any given accuracy when the points are sampled from a two-dimensional surface.     

Remarks on a displacement-rank inversion method for Toeplitz systems

Comments are made regarding the implementation of a Toeplitz-matrix inversion algorithm described by Bitmead and Anderson in [1]. We show that although the algorithm is asymptotically efficient with O(N(logN)²) operations, it requires a 10⁶×10⁶ matrix to break even with the class of algorithms whose operation count is of the order of O(N²) (as found in [4]).     

O(n2.5) time algorithms for the subgraph homeomorphism problem on trees

The complexity of the subgraph homeomorphism problems have been open. We show O(n^2.5) time algorithms when the problems are restricted to trees, directed or undirected. The algorithm can be applied to the subtree isomorphism problem for unrooted trees with the same complexity, and improves over Reyner's O(n^3.5) algorithm for the subtree isomorphism problem.     

An exact algorithm for MAX-CUT in sparse graphs

We study exact algorithms for the MAX-CUT problem. Introducing a new technique, we present an algorithmic scheme that computes a maximum cut in graphs with bounded maximum degree. Our algorithm runs in time O^*(2^(1-(2/Δ))n). We also describe a MAX-CUT algorithm for general graphs. Its time complexity is O^*(2^mn/(m+n)). Both algorithms use polynomial space.     

Optimized look-ahead recurrences for adjacent rows in the Padé table

A new look-ahead algorithm for recursively computing Padé approximants is introduced. It generates a subsequence of the Padé approximants on two adjacent rows (defined by fixed numerator degree) of the Padé table. Its two basic versions reduce to the classical Levinson and Schur algorithms if no look-ahead is required. The new algorithm can be viewed as a combination of the look-ahead sawtooth and the look-ahead Levinson and Schur algorithms that we proposed before, but now the look-ahead step size is minimal (as in the sawtooth version) and the computational costs are as low as in the least expensive competing algorithms (including our look-ahead Levinson and Schur algorithms). The underlying recurrences link well-conditioned basic pairs,i.e., pairs of sufficiently different neighboring Padé forms.The algorithm can be used to solve Toeplitz systems of equationsTx = b. In this application it comes in several versions: anO(N ²) Levinson-type form, anO(N ²) Schur-type form, and a superfastO(N log² N) Schur-type version. As an option of the first two versions, the corresponding block LDU decompositions ofT ^–1 orT, respectively, can be found.     

Constructing strength three covering arrays with augmented annealing

A covering arrayCA(N;t,k,v) is an N×k array such that every N×t sub-array contains all t-tuples from v symbols at least once, where t is the strength of the array. One application of these objects is to generate software test suites to cover all t-sets of component interactions. Methods for construction of covering arrays for software testing have focused on two main areas. The first is finding new algebraic and combinatorial constructions that produce smaller covering arrays. The second is refining computational search algorithms to find smaller covering arrays more quickly. In this paper, we examine some new cut-and-paste techniques for strength three covering arrays that combine recursive combinatorial constructions with computational search; when simulated annealing is the base method, this is augmented annealing. This method leverages the computational efficiency and optimality of size obtained through combinatorial constructions while benefiting from the generality of a heuristic search. We present a few examples of specific constructions and provide new bounds for some strength three covering arrays.     

A note on the partitioning shortest path algorithm

Recently Glover, Klingman and Phillips proposed the Partitioning Shortest Path (PSP) algorithm. The PSP algorithm includes as variants most of the known algorithms for the shortest path problem. In a subsequent paper, together with Schneider, they proposed several variants of the PSP and conducted computational tests. Three of the variants were the first polynomially bounded shortest path algoriths to maintain sharp labels as defined by Shier and Witzgall. Two of these variants had computational complexity O(|N|²|A|), the other O(|N|³). In this note, we add a new step to the PSP algorithm resulting in new variants also scanning from sharp labels and having computational complexity O(|N|³) for two of them and O(|N|²) for the other. This new step also provides a test for the early detection of negative length cycles.     

Displacement ranks of matrices and linear equations

It takes of the order of N³ operations to solve a set of N linear equations in N unknowns or to invert the corresponding coefficient matrix. When the underlying physical problem has some time- or shift-invariance properties, the coefficient matrix is of Toeplitz (or difference or convolution) type and it is known that it can be inverted with O(N²) operations. However non-Toeplitz matrices often arise even in problems with some underlying time-invariance, e.g., as inverses or products or sums of products of possibly rectangular Toeplitz matrices. These non-Toeplitz matrices should be invertible with a complexity between O(N²) and O(N³). In this paper we provide some content for this feeling by introducing the concept of displacement ranks, which serve as a measure of how ‘close’ to Toeplitz a given matrix is.     

Bound reduction using pairs of linear inequalities

We describe a procedure to reduce variable bounds in mixed integer nonlinear programming (MINLP) as well as mixed integer linear programming (MILP) problems. The procedure works by combining pairs of inequalities of a linear programming (LP) relaxation of the problem. This bound reduction procedure extends the feasibility based bound reduction technique on linear functions, used in MINLP and MILP. However, it can also be seen as a special case of optimality based bound reduction, a method to infer variable bounds from an LP relaxation of the problem. For an LP relaxation with m constraints and n variables, there are O(m ²) pairs of constraints, and a naïve implementation of our bound reduction scheme has complexity O(n ³) for each pair. Therefore, its overall complexity O(m ² n ³) can be prohibitive for relatively large problems. We have developed a more efficient procedure that has complexity O(m ² n ²), and embedded it in two Open-Source solvers: one for MINLP and one for MILP. We provide computational results which substantiate the usefulness of this bound reduction technique for several instances.     

Advanced combinations of splitting–shooting–integrating methods for digital image transformations

An image consists of many discrete pixels with greyness of different levels, which can be quantified by greyness values. The greyness values at a pixel can also be represented by an integral as the mean of continuous greyness functions over a small pixel region. Based on such an idea, the discrete images can be produced by numerical integration; several efficient algorithms are developed to convert images under transformations. Among these algorithms, the combination of splitting–shooting–integrating methods (CSIM) is most promising because no solutions of nonlinear equations are required for the inverse transformation. The CSIM is proposed in [6] to facilitate images and patterns under a cycle transformations T⁻¹T, where T is a nonlinear transformation. When a pixel region in two dimensions is split into N² subpixels, convergence rates of pixel greyness by CSIM are proven in [8] to be only O(1/N). In [10], the convergence rates O_p(1/N^1.5) in probability and O_p(1/N²) in probability using a local partition are discovered. The CSIM is well suited to binary images and the images with a few greyness levels due to its simplicity. However, for images with large (e.g., 256) multi-greyness levels, the CSIM still needs more CPU time since a rather large division number is needed.In this paper, a partition technique for numerical integration is proposed to evaluate carefully any overlaps between the transformed subpixel regions and the standard square pixel regions. This technique is employed to evolve the CSIM such that the convergence rate O(1/N²) of greyness solutions can be achieved. The new combinations are simple to carry out for image transformations because no solutions of nonlinear equations are involved in, either. The computational figures for real images of 256×256 with 256 greyness levels display that N=4 is good enough for real applications. This clearly shows validity and effectiveness of the new algorithms in this paper.     

Fast numerical solution of fredholm integral equations with stationary kernels

A fast recursive matrix method for the numerical solution of Fredholm integral equations with stationary kernels is derived. IfN denotes the number of nodal points, the complexity of the algorithm isO(N ²), which should be compared toO(N ³) for conventional algorithms for solving such problems. The method is related to fast algorithms for inverting Toeplitz matrices.Applications to equations of the first and second kind as well as miscellaneous problems are discussed and illustrated with numerical examples. These show that the theoretical improvement in efficiency is indeed obtained, and that no problems with numerical stability or accuracy are encountered.     

On the enumeration of chains in regular chain-groups

Let N be a regular chain-group on E (see W. T. Tutte, Canad. J. Math.8 (1956), 13–28); for instance, N may be the group of integer flows or tensions of a directed graph with edge-set E). It is known that the number of proper Z_λ-chains of N (λ ∈ Z, λ ≥ 2) is given by a polynomial in λ, P(N, λ) (when N is the chain-group of integer tensions of the connected graph G, λP(N, λ) is the usual chromatic polynomial of G). We prove the formula: P(N, λ) = Σ_{[E′]∈O(N)⁺/～}Q(R_[E′](N), λ), where O(N)⁺ is the set of orientations of N with a proper positive chain, ～ is a simple equivalence relation on O(N)⁺ (sequence of reversals of positive primitive chains), and Q(R_[E′](N), λ) is the number of chains with values in [1, λ ? 1] in any reorientation of N associated to an element of [E′]. Moreover, each term Q(R_[E′](N), λ) is a polynomial in λ. As applications we obtain: P(N, 0) = (?1)^r(N)∥O(N)⁺/～∥; P(N, ?1) = (?1)^r(N)∥O(N)⁺∥ (a result first proved by Brylawski and Lucas); P(N, λ + 1) ≥ P(N, λ) for λ ≥ 2, λ ∈ Z. Our result can also be considered as a refinement of the following known fact: A regular chain-group N has a proper Z_λ-chain iff it has a proper chain in [?λ + 1, λ ? 1].     

A Two-Echelon Inventory Optimization Model with Demand Time Window Considerations

This paper studies a two-echelon dynamic lot-sizing model with demand time windows and early and late delivery penalties. The problem is motivated by third-party logistics and vendor managed inventory applications in the computer industry where delivery time windows are typically specified under a time definite delivery contract. Studying the optimality properties of the problem, the paper provides polynomial time algorithms that require O(T ³) computational complexity if backlogging is not allowed and O(T ⁵) computational complexity if backlogging is allowed.