Abstract: The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached. In order to improve convergence for nearly-flat meshes with small features, such as inscribed surfaces, we introduce a new variant based on uniform sampling of the space of normals. We conclude by proposing a combination of ICP variants optimized for high speed. We demonstrate an implementation that is able to align two range images in a few tens of milliseconds, assuming a good initial guess. This capability has potential application to real-time 3D model acquisition and model-based tracking.

Abstract: This paper proposes an efficient numerical algorithm to compute the optimal input distribution that maximizes the sum capacity of a Gaussian multiple-access channel with vector inputs and a vector output. The numerical algorithm has an iterative water-filling interpretation. The algorithm converges from any starting point, and it reaches within 1/2 nats per user per output dimension from the sum capacity after just one iteration. The characterization of sum capacity also allows an upper bound and a lower bound for the entire capacity region to be derived.

Abstract: The FETI method and its two-level extension (FETI-2) are two numerically scalable domain decomposition methods with Lagrange multipliers for the iterative solution of second-order solid mechanics and fourth-order beam, plate and shell structural problems, respectively.The FETI-2 method distinguishes itself from the basic or one-level FETI method by a second set of Lagrange multipliers that are introduced at the subdomain cross-points to enforce at each iteration the exact continuity of a subset of the displacement field at these specific locations. In this paper, we present a dual–primal formulation of the FETI-2 concept that eliminates the need for that second set of Lagrange multipliers, and unifies all previously developed one-level and two-level FETI algorithms into a single dual–primal FETI-DP method. We show that this new FETI-DP method is numerically scalable for both second-order and fourth-order problems. We also show that it is more robust and more computationally efficient than existing FETI solvers, particularly when the number of subdomains and/or processors is very large. Copyright © 2001 John Wiley & Sons, Ltd.

Abstract: A linear-correction least-squares estimation procedure is proposed for the source localization problem under an additive measurement error model. The method, which can be easily implemented in a real-time system with moderate computational complexity, yields an efficient source location estimator without assuming a priori knowledge of noise distribution. Alternative existing estimators, including likelihood-based, spherical intersection, spherical interpolation, and quadratic-correction least-squares estimators, are reviewed and comparisons of their complexity, estimation consistency and efficiency against the Cramer-Rao lower bound are made. Numerical studies demonstrate that the proposed estimator performs better under many practical situations.

Abstract: A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.

Abstract: Superresolution reconstruction produces a high-resolution image from a set of low-resolution images. Previous iterative methods for superresolution had not adequately addressed the computational and numerical issues for this ill-conditioned and typically underdetermined large scale problem. We propose efficient block circulant preconditioners for solving the Tikhonov-regularized superresolution problem by the conjugate gradient method. We also extend to underdetermined systems the derivation of the generalized cross-validation method for automatic calculation of regularization parameters. The effectiveness of our preconditioners and regularization techniques is demonstrated with superresolution results for a simulated sequence and a forward looking infrared (FLIR) camera image sequence.

Abstract: We consider a wide class of iterative methods arising in numerical mathematics and optimization that are known to converge only weakly. Exploiting an idea originally proposed by Haugazeau, we present a simple modification of these methods that makes them strongly convergent without additional assumptions. Several applications are discussed.

Abstract: Recently the problem of determining the best, in the least-squares sense, rank-1 approximation to a higher-order tensor was studied and an iterative method that extends the well-known power method for matrices was proposed for its solution. This higher-order power method is also proposed for the special but important class of supersymmetric tensors, with no change. A simplified version, adapted to the special structure of the supersymmetric problem, is deemed unreliable, as its convergence is not guaranteed. The aim of this paper is to show that a symmetric version of the above method converges under assumptions of convexity (or concavity) for the functional induced by the tensor in question, assumptions that are very often satisfied in practical applications. The use of this version entails significant savings in computational complexity as compared to the unconstrained higher-order power method. Furthermore, a novel method for initializing the iterative process is developed which has been observed to yield an estimate that lies closer to the global optimum than the initialization suggested before. Moreover, its proximity to the global optimum is a priori quantifiable. In the course of the analysis, some important properties that the supersymmetry of a tensor implies for its square matrix unfolding are also studied.

Abstract: We present a variant of the classical weighted least-squares stabilization for the Stokes equations. Compared to the original formulation, the new method has improved accuracy properties, especially near boundaries. Furthermore, no modification of the right-hand side is needed, and implementation is simplified, especially for generalizations to more complicated equations. The approach is based on local projections of the residual terms which are used in order to achieve consistency of the method, avoiding local evaluation of the strong form of the differential operator. We prove stability and give a priori and a posteriori error estimates. We show convergence of an iterative method which uses a simplified stabilized discretization as preconditioner. Numerical experiments indicate that the approach presented is at least as accurate as the original method, but offers some algorithmic advantages. The ideas presented here also apply to the Navier–Stokes equations. This is the topic of forthcoming work.

Abstract: The convergence of a penalty method for solving the discrete regularized American option valuation problem is studied. Sufficient conditions are derived which both guarantee convergence of the nonlinear penalty iteration and ensure that the iterates converge monotonically to the solution. These conditions also ensure that the solution of the penalty problem is an approximate solution to the discrete linear complementarity problem. The efficiency and quality of solutions obtained using the implicit penalty method are compared with those produced with the commonly used technique of handling the American constraint explicitly. Convergence rates are studied as the timestep and mesh size tend to zero. It is observed that an implicit treatment of the American constraint does not converge quadratically (as the timestep is reduced) if constant timesteps are used. A timestep selector is suggested which restores quadratic convergence.

Abstract: We consider the application of the conjugate gradient method to the solution of large equality constrained quadratic programs arising in nonlinear optimization. Our approach is based implicitly on a reduced linear system and generates iterates in the null space of the constraints. Instead of computing a basis for this null space, we choose to work directly with the matrix of constraint gradients, computing projections into the null space by either a normal equations or an augmented system approach. Unfortunately, in practice such projections can result in significant rounding errors. We propose iterative refinement techniques, as well as an adaptive reformulation of the quadratic problem, that can greatly reduce these errors without incurring high computational overheads. Numerical results illustrating the efficacy of the proposed approaches are presented.

Abstract: VLST implementation complexities of soft-input soft-output (SISO) decoders are discussed. These decoders are used in iterative algorithms based on Turbo codes or Low Density Parity Check (LDPC) codes, and promise significant bit error performance advantage over conventionally used partial-response maximum likelihood (PRML) systems, at the expense of increased complexity. This paper analyzes the requirements for computational hardware and memory, and provides suggestions for reduced-complexity decoding and reduced control logic. Serial concatenation of interleaved codes, using an outer block code with a partial response channel acting as an inner encoder, is of special interest for magnetic storage applications.

Abstract: Statistical conditional optimization criteria lead to the development of an iterative algorithm that starts from the matched filter (or constraint vector) and generates a sequence of filters that converges to the minimum-variance-distortionless-response (MVDR) solution for any positive definite input autocorrelation matrix. Computationally, the algorithm is a simple, noninvasive, recursive procedure that avoids any form of explicit autocorrelation matrix inversion, decomposition, or diagonalization. Theoretical analysis reveals basic properties of the algorithm and establishes formal convergence. When the input autocorrelation matrix is replaced by a conventional sample-average (positive definite) estimate, the algorithm effectively generates a sequence of MVDR filter estimators; the bias converges rapidly to zero and the covariance trace rises slowly and asymptotically to the covariance trace of the familiar sample-matrix-inversion (SMI) estimator. In fact, formal convergence of the estimator sequence to the SMI estimate is established. However, for short data records, it is the early, nonasymptotic elements of the generated sequence of estimators that offer favorable bias covariance balance and are seen to outperform in mean-square estimation error, constraint-LMS, RLS-type, orthogonal multistage decomposition, as well as plain and diagonally loaded SMI estimates. An illustrative interference suppression example is followed throughout this presentation.

Abstract: A greedy randomized adaptive search procedure (GRASP) is a heuristic method that has shown to be very powerful in solving combinatorial problems. In this paper we apply GRASP to solve the transmission network expansion problem. This procedure is an expert iterative sampling technique that has two phases for each iteration. The first, construction phase, finds a feasible solution for the problem. The second phase, a local search, seeks for improvements on construction phase solution by a local search. The best solution over all GRASP iterations is chosen as the result.

Abstract: In this paper, we propose preconditioned Krylov-subspace iterative methods to perform efficient DC and transient simulations for large-scale linear circuits with an emphasis on power delivery circuits. We also prove that a circuit with inductors can be simplified from MNA to NA format, and the matrix becomes an s.p.d. matrix. This property makes it suitable for the conjugate gradient with incomplete Cholesky decomposition as the preconditioner, which is faster than other direct and iterative methods. Extensive experimental results on large-scale industrial power grid circuits show that our method is over 200 times faster for DC analysis and around 10 times faster for transient simulation compared to SPICE3. Furthermore, our algorithm reduces over 75% of memory usage than SPICE3 while the accuracy is not compromised.

Abstract: A one-dimensional iterative chaotic map with infinite collapses within symmetrical region [-1, O)/spl cup/(O, +1] is proposed. The stability of fixed points and that around the singular point are analyzed. Higher Lyapunov exponents of proposed map show stronger chaotic characteristics than some iterative and continuous chaotic models usually used. There exist inverse bifurcation phenomena and special main periodic windows at certain positions shown in the bifurcation diagram, which can explain the generation mechanism of chaos. The chaotic model with good properties can be generated if choosing the parameter of the map properly. Stronger inner pseudorandom characteristics can also be observed through /spl chi//sup 2/ test on the supposition of even distribution. This chaotic model may have many advantages in practical use.

Abstract: An efficient inverse-scattering algorithm is developed to reconstruct both the permittivity and conductivity profiles of two-dimensional (2D) dielectric objects buried in a lossy earth using the distorted Born iterative method (DBIM). In this algorithm, the measurement data are collected on (or over) the air-earth interface for multiple transmitter and receiver locations at single frequency. The nonlinearity due to the multiple scattering of pixels to pixels, and pixels to the air-earth interface has been taken into account in the iterative minimization scheme. At each iteration, a conjugate gradient (CG) method is chosen to solve the linearized problem, which takes the calling number of the forward solver to a minimum. To reduce the CPU time, the forward solver for buried dielectric objects is implemented by the CG method and fast Fourier transform (FFT). Numerous numerical examples are given to show the convergence, stability, and error tolerance of the algorithm.

Abstract: The maximum-likelihood (ML) approach in emission tomography provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms The expectation-maximization (EM) algorithm is an iterative algorithm for maximizing the Poisson likelihood in emission computed tomography that became very popular for solving the ML problem because of its attractive theoretical and practical properties Recently, (Browne and DePierro, 1996 and Hudson and Larkin, 1991) block sequential versions of the EM algorithm that take advantage of the scanner's geometry have been proposed in order to accelerate its convergence In Hudson and Larkin, 1991, the ordered subsets EM (OS-EM) method was applied to the hit problem and a modification (OS-GP) to the maximum a posteriori (MAP) regularized approach without showing convergence In Browne and DePierro, 1996, we presented a relaxed version of OS-EM (RAMLA) that converges to an ML solution In this paper, we present an extension of RAMLA for MAP reconstruction We show that, if the sequence generated by this method converges, then it must converge to the true MAP solution Experimental evidence of this convergence is also shown To illustrate this behavior we apply the algorithm to positron emission tomography simulated data comparing its performance to OS-GP

Abstract: In mapping the k-means algorithm to FPGA hardware, we examined algorithm level transforms that dramatically increased the achievable parallelism. We apply the k-means algorithm to multi-spectral and hyper-spectral images, which have tens to hundreds of channels per pixel of data. K-means is an iterative algorithm that assigns assigns to each pixel a label indicating which of K clusters the pixel belongs to.K-means is a common solution to the segmentation of multi-dimensional data. The standard software implementation of k-means uses floating-point arithmetic and Euclidean distances. Floating point arithmetic and the multiplication-heavy Euclidean distance calculation are fine on a general purpose processor, but they have large area and speed penalties when implemented on an FPGA. In order to get the best performance of k-means on an FPGA, the algorithm needs to be transformed to eliminate these operations. We examined the effects of using two other distance measures, Manhattan and Max, that do not require multipliers. We also examined the effects of using fixed precision and truncated bit widths in the algorithm.It is important to explore algorithmic level transforms and tradeoffs when mapping an algorithm to reconfigurable hardware. A direct translation of the standard software implementation of k-means would result in a very inefficient use of FPGA hardware resources. Analysis of the algorithm and data is necessary for a more efficient implementation. Our resulting implementation exhibits approximately a 200 times speed up over a software implementation.

Abstract: A numerical procedure for the calculation of buoyancy-driven flows using the finite-volume approach is presented. It is based on an extension of the operator-splitting procedure PISO of Issa [1] to the specific case in which the coupling between velocity/pressure and temperature is important, as is the case in problems involving free-convection flows. A comparison of the proposed procedure with a standard iterative method shows improvement both in terms of computing speed (a factor of 2.1 to 4.1) and robustness.

Abstract: In this paper, we focus on a useful modification of the decomposition method by He et al. (Ref. 1). Experience on applications has shown that the number of iterations of the original method depends significantly on the penalty parameter. The main contribution of our method is that we allow the penalty parameter to vary automatically according to some self-adaptive rules. As our numerical simulations indicate, the modified method is more flexible and efficient in practice. A detailed convergence analysis of our method is also included.

Abstract: Most preconditioned iterative methods apply to both real- and complex-valued linear systems. At the same time, most iterative linear solver packages available today focus exclusively on real-valued systems or deal with complex-valued systems as an afterthought. By recasting the complex problem in a real formulation, a real-valued solver can be applied to the equivalent real system. On one hand, real formulations have been dismissed due to their unfavorable spectral properties. On the other hand, using an equivalent preconditioned real formulation can be very effective. We give theoretical and experimental evidence that an equivalent real formulation is useful in a number of practical situations. Furthermore, we show how to use the advanced features of modern solver packages to formulate equivalent real preconditioners that are computationally efficient and mathematically identical to their complex counterparts. The effectiveness of equivalent real formulations is demonstrated by solving ill-conditioned complex-valued linear systems for a variety of large scale applications. Moreover, the circumstances under which certain equivalent real formulations are competitive is more clearly delineated.