Incentive compatible control using iterative decomposition algorithms

This paper presents an iterative geometric mean decomposition (IGMD) algorithm for multiple-input-multiple-output (MIMO) wireless communications. In contrast to the conventional geometric mean decomposition (GMD) algorithm, the proposed IGMD does not require the explicit Kth root computation in the preprocessing stage but depends on a carefully constructed iterative procedure that generates the GMD in its limit. We prove analytically that the proposed IGMD is guaranteed to converge to the exact GMD under certain sufficient conditions, and propose three different constructions achieving this condition. Both numerical simulations and complexity analysis of the proposed IGMD have been conducted and compared with the conventional GMD. Simulation results show that our new IGMD algorithm effectively reduces the complexity overhead and hence is more advantageous for low-complexity implementations.

Computing solutions to moral-hazard programs using the Dantzig–Wolfe decomposition algorithm

Application of regional earthquake mitigation optimization

The relationship between solutions of the full master program of the Dantzig-Wolfe decomposition routine and the originally given problem is considered, and a criterion is established which states whether a basic feasible solution of the former corresponds to one of the latter.

A Dantzig–Wolfe decomposition algorithm for linear economic model predictive control of dynamically decoupled subsystems

The authors present an approach to the linear programming state estimation problem that is suitable for large-scale interconnected power systems. It uses the Dantzig-Wolfe decomposition algorithm. The performance of the method is enhanced by using a multiple column strategy and several other modifications to the original algorithm. Scaling techniques are used to guarantee a reliable solution. Test results on standard test systems are used to illustrate the usefulness of the method and its viability for parallel processor implementation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In maintenance of a water distribution network, the allocation of limited funds for maintenance among a network's pipes and the decision to repair or replace a single pipe when failure occurs are the most important issues. Although some researchers in the past have addressed these issues, more work is needed to support water authorities in making maintenance decisions. In this research paper, a mathematical model is developed that aims to support the decision to allocate funds among pipes of the network as well as the decision to repair or replace the pipes in the state of failure. The objective function of the model is to maximize the total weighted long-run availability of the whole system. The concept of hydraulic reliability is employed to determine the weight of pipes in the maintenance program. The deterioration behavior of the pipe is depicted by a semi-Markov process, and the Dantzig–Wolfe decomposition algorithm is applied to deal with the large-scale characteristic of the resulting program.

Narrow-band interference (NBI) and Wide-band interference (WBI) are critical issues for synthetic aperture radar (SAR), which degrades the imaging quality severely. Since some complex signals can be modeled as linear frequency modulated (LFM) signals within a short time, LFM-WBI and NBI are mainly discussed in this paper. Due to its excellent energy concentration and useful properties (i.e., auto-terms pass through the origin of Delay-Doppler plane while cross-terms are away from it), a novel nonparametric interference suppression method using Delay-Doppler iterative decomposition algorithm is proposed. This algorithm consists of three stages. First, we present signal synthesis method (SSM) from ambiguity function (AF) and cross ambiguity function (CAF) based on the matrix rearrangement and eigenvalue decomposition. Compared with traditional SSM from Wigner distribution (WD), the proposed SSM can synthesize a signal faster and more accurately. Then, based on unique properties in Delay-Doppler domain, a mask algorithm is applied for interference identification and extraction using Radon and its inverse transformation. Finally, a signal iterative decomposition algorithm (IDA) is utilized to subtract the largest interference from the received signal one by one. After that, a well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrate that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible.

Chemical shift based methods are often used to achieve uniform water-fat separation that is insensitive to Bo inhomogeneities. Many spin-echo (SE) or fast SE (FSE) approaches acquire three echoes shifted symmetrically about the SE, creating time-dependent phase shifts caused by water-fat chemical shift. This work demonstrates that symmetrically acquired echoes cause artifacts that degrade image quality. According to theory, the noise performance of any water-fat separation method is dependent on the proportion of water and fat within a voxel, and the position of echoes relative to the SE. To address this problem, we propose a method termed "iterative decomposition of water and fat with echo asymmetric and least-squares estimation" (IDEAL). This technique combines asymmetrically acquired echoes with an iterative least-squares decomposition algorithm to maximize noise performance. Theoretical calculations predict that the optimal echo combination occurs when the relative phase of the echoes is separated by 2pi/3, with the middle echo centered at pi/2+pik (k=any integer), i.e., (-pi/6+pik, pi/2+pik, 7pi/6+pik). Only with these echo combinations can noise performance reach the maximum possible and be independent of the proportion of water and fat. Close agreement between theoretical and experimental results obtained from an oil-water phantom was observed, demonstrating that the iterative least-squares decomposition method is an efficient estimator.

The medical practice of euthanasia in Belgium and The Netherlands: Legal notification, control and evaluation procedures

In recent years, compressed sensing (CS) has been proposed and successfully applied to speed up the acquisition in dynamic MRI. However, how to improve the quality of dynamic MRI is still a worthwhile question. Recently, a low-rank plus sparse (L+S) matrix decomposition model with S 1 and L 1 regularizations is proposed for reconstruction of under-sampled dynamic MRI with separation of background and dynamic components. It can effectively detect dynamic information in the process of imaging. In our work, we propose an improved L+S matrix decomposition model with S 1/2 and L 1/2 regularizations in order to improve the quality of original separation. To solve the model, we use an iterative half-thresholding decomposition algorithm. Finally, empirical results show that the new model can produce better performance and capture more completed dynamic information than the existing model.

In this paper, a Wireless Sensor Network (WSN) deployment problem is posed: in a two-level heterogeneous WSN, we need to optimally determine the location of cluster-heads in order to minimize communication power. We require that each sensor node connects to at least p cluster-heads for reliability, and each cluster-head can accept at most q connections. The optimization problem in formulated as a Mixed Integer Nonlinear Programming (MINLP) problem. To overcome the fact that a MINLP solver fails to solve large-scale cases or obtain a global optimum, we propose an iterative decomposition algorithm and use a randomized multi-start technique for global optimization. We also propose an incremental deployment approach and use it to solve the original problem as if the WSN is built incrementally. Numerical results show that the decomposition algorithm is very efficient. While the incremental deployment method is slower in each run, it produces a better solution distribution compared to the pure multi-start approach. Both, however, are capable of solving large-scale problems.

The echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.

Lin et al. propose the iterative Toeplitz filters algorithm as an alternative iterative algorithm for Empirical Mode Decomposition (EMD). In this alternative algorithm, the average of the upper and lower envelopes is replaced by certain "moving average" obtained through a low-pass filter. Performing the traditional sifting algorithm with such moving averages is equivalent to iterating certain convolution filters (finite length Toeplitz filters). This paper studies the convergence of this algorithm for signals of continuous variables, and proves that the limit function of this iterative algorithm is an ideal high-pass filtering process.

The extremal problem arises in theDantzig-Wolfe decomposition algorithm for linear programming (LP) problems. This note exhibits a particular LP problem, the associated extremal problem of which has an interesting interpretation. That particular problem is a special feed mix blending problem. The columns of the associated extremal problem may be interpreted as recipies for blending the different feed mixes.