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Highlights • A sharing multi-view feature selection method which combines the specificity of views and the common objective is proposed. • To effectively select features from the high dimensional data via sharing strategy and ADMM. • To speed up the calculation by decomposing the large scale optimization problem into small scale sub-problems. • The comparison experiments with several state-of-the-art feature selection methods show its effectiveness. Abstract The matrix-based multi-view feature selection, which can integrate the information of multiple views for selecting representative features, has attracted wide attention in recent years. In this paper, we propose a novel supervised sharing multi-view feature selection method. The proposed method makes all views share a common penalty that regresses samples to their labels. Meanwhile, it adopts the structured sparsity-inducing norm to implement sparsity for each view. The proposed method considers not only the complementary of different views, but also the specificity of each view instead of concatenating all views into high-dimensional vectors. In addition, the proposed model can be decomposed into N small scale subproblems (where N is the number of views) and solved efficiently via Alternating Direction Method of Multipliers (ADMM), especially for high-dimensional large scale data sets. The comparison experiments with several state-of-the-art feature selection methods show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The performance of traditional reconstruction-based robust adaptive beamformers may degrade when the array is not well calibrated. This performance degradation is mainly caused by the Capon spectrum estimator which can not estimate the spatial power spectrum accurately. In contrast to existing approaches, we propose two new reconstruction-based robust adaptive beamformers by using the accurate iterative adaptive approach (IAA) spectrum to combat the covariance matrix uncertainties and the steering vector mismatches. The first one employs the low-complexity IAA (IAA-LC) algorithm to obtain the interference power estimates and reconstruct the interference-plus-noise covariance matrix (INCM) with the computational complexity further reduced. The second one reconstructs the INCM by updating the power estimates corresponding to each interference steering vector with the use of knowledge-aided IAA (IAA-KA) algorithm. The desired signal steering vector is corrected by searching for the direction corresponding to the maximum power, which circumvents the use of optimization program. Simulation results show that our proposed beamformers outperform the others and can achieve the robust performance in the cases of array model mismatches. [ABSTRACT FROM AUTHOR]

In problems involving control of financial processes, it is usually complicated to quantify exactly the state variables. It could be expensive to acquire the exact value of a given state, even if it may be physically possible to do so. In such cases it may be interesting to support the decision-making process on inaccurate information pertaining to the system state. In addition, for modeling real-world application, it is necessary to compute the values of the parameters of the environment (transition probabilities and observation probabilities) and the reward functions, which are typically, hand-tuned by experts in the field until it has acquired a satisfactory value. This results in an undesired process. To address these shortcomings, this paper provides a new Reinforcement Learning (RL) framework for computing the mean-variance customer portfolio with transaction costs in controllable Partially Observable Markov Decision Processes (POMDPs). The solution is restricted to finite state, action, observation sets and average reward problems. For solving this problem, a controller/actor-critic architecture is proposed, which balance the difficult tasks of exploitation and exploration of the environment. The architecture consists of three modules: controller, fast-tracked portfolio learning and an actor-critic module. Each module involves the design of a convergent Temporal Difference (TD) learning algorithm. We employ three different learning rules to estimate the real values of: (a) the transition matrices π ^ (j | i k) (t) , (b) the rewards J ^ (i j k) (t) and (c) the resources destined for carrying out a promotion ζ ^ (i ^ | k ^) (t). We present a proof for the estimated transition matrix rule π ^ (j | i k) (t) and showing that it converges when t → ∞. For solving the optimization programming problem we extend the c-variable method for partially observable Markov chains. The c-variable is conceptualized as joint strategy given by the product of the control policy, the observation kernel Q(y|s) and the stationary distribution vector. A major advantage of this procedure is that it can be implemented efficiently for real settings in controllable POMDP. A numerical example illustrates the results of the proposed method. [ABSTRACT FROM AUTHOR]

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements. [ABSTRACT FROM PUBLISHER]

Energy storage systems are one of the recent technologies utilized in order to achieve the stability and reliability of the power system with the integration of renewable energy sources. Energy storage systems can enhance system performance by means of the proper energy management. First, this paper presents a probabilistic modeling strategy for wind power and system demand. The modeling strategy utilizes Monte Carlo simulations to accurately consider the intermittent nature of wind generation and demand. Second, a linear programming optimization approach is performed to maximize system arbitrage benefits through optimizing operation of energy storage systems. Finally, two novel algorithms for optimal allocation of renewable energy resources and energy storage elements are proposed. The proposed algorithms are dependent on Big Bang Big Crunch optimization method and integrating the results of the modeling strategy with the optimization problem. The presented algorithms aim to minimize the net present value of energy loss cost in distribution systems. The proposed modeling strategy and optimization algorithms are implemented in MATLAB environment and tested on IEEE 33 bus system. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In the paper, a necessary and sufficient criterion it provided such that any local optimal solution is also global in a not necessarily differentiable constrained optimization problem. This criterion is compared to others earlier appeared in the literature, which are sufficient but not necessary for a local optimal solution to be global. The importance of the established criterion is illustrated by suitable examples of nonconvex optimization problems presented in the paper. [ABSTRACT FROM AUTHOR]

Using a simulation-based approach, sorptive barrier design can be expressed as a nonlinear and mixed-integer optimization problem, and metaheuristic searching algorithms are suitable optimization methods to find optimal configurations of design. A recently proposed neighborhood field optimization (NFO) algorithm is applied to deal with the sorptive barrier design problem. NFO is originally proposed for continuous optimization problems, then, it is extended to binary NFO for discontinuous optimization problems. In this paper, integer NFO (INFO) is proposed by using forward and backward transformations. For the sorptive barrier design, NFO variants are compared with genetic algorithms and the best performer reported previously. Based on statistical analysis, NFO variants show better performance than GA variants in terms of accuracy and convergence speed, and INFO improves the best known results on all test instances. It can be concluded that the proposed INFO is suitable for the sorptive barrier design with significant performance improvement. [ABSTRACT FROM AUTHOR]

This paper presents a neurodynamic approach with a recurrent neural network for solving convex optimization problems with general constraint. It is proved that for any initial point, the state of the proposed neural network reaches the constraint set in finite time, and converges to an optimal solution of the convex optimization problem finally. In contrast to the existing related neural networks, the convergence rate of the state of the proposed neural network can be calculated quantitatively via the Łojasiewicz exponent under some mild assumptions. As applications, we estimate explicitly some Łojasiewicz exponents to show the convergence rate of the state of the proposed neural network for solving convex quadratic optimization problems. And some numerical examples are given to demonstrate the effectiveness of the proposed neural network. [ABSTRACT FROM AUTHOR]

This study suggests a novel and mathematical dynamic index for greatest coordinated control of Pulse Width Modulation based AC link Series Compensator (PWMSC) and Power System Stabilizers (PSS) in multi-machine networks. The planned new dynamic index is developed as a fitness function to reinforce the oscillations damping under totally different loading conditions. A nonlinear constrained optimization programming is applied for reducing the dynamic index based mostly from critical eigenvalues of the systems. The robustness of the coordinated control is reached directly through extending numerous loading conditions from that of the nominal case. The best proficiency of the proposed control is showed through simulations on a four-machine and sixteen-machine power networks for various conditions. Linear and nonlinear analysis presents the high priority of the suggested dynamic index in tuning of the parameters controller. [ABSTRACT FROM AUTHOR]

In this work, we consider the problem of high-resolution estimation of the parameters detailing an N -dimensional ( N -D) signal consisting of an unknown number of exponentially decaying sinusoidal components. Since such signals are not sparse in an oversampled Fourier matrix, earlier approaches typically exploit large dictionary matrices that include not only a finely spaced frequency grid, but also a grid over the considered damping factors. Even in the 2-D case, the resulting dictionary is typically very large, resulting in a computationally cumbersome optimization problem. Here, we introduce a sparse modeling framework for N -dimensional exponentially damped sinusoids using the Kronecker structure inherent in the model. Furthermore, we introduce a novel dictionary learning approach that iteratively refines the estimate of the candidate frequency and damping coefficients for each component, thus allowing for smaller dictionaries, and for frequency and damping parameters that are not restricted to a grid. The performance of the proposed method is illustrated using simulated data, clearly showing the improved performance as compared to previous techniques. [ABSTRACT FROM AUTHOR]

Background: The study of human walking patterns mainly focuses on how control affects walking because control schemes are considered to be dominant in human walking.Objective: This study proposes that not only fine control schemes but also optimized body segment parameters are responsible for humans' low-energy walking.Methods: A passive dynamic walker provides the possibility of analyzing the effect of parameters on walking efficiency because of its ability to walk without any control. Thus, a passive dynamic walking model with a relatively human-like structure was built, and a parameter optimization process based on the gait sensitivity norm was implemented to determine the optimal mechanical parameters by numerical simulation.Results: The results were close to human body parameters, thus indicating that humans can walk under a passive pattern based on their body segment parameters. A quasi-passive walking prototype was built on the basis of the optimization results. Experiments showed that a passive robot with optimized parameters could walk on level ground with only a simple hip actuation.Conclusion: This result implies that humans can walk under a passive pattern based on their body segment parameters with only simple control strategy implying that humans can opt to walk instinctively under a passive pattern. [ABSTRACT FROM AUTHOR]

Real world engineering design problems are usually characterized by the presence of many conflicting objectives. In this paper, we will make an approach to solve multi-objective structural model using parameterized t-norms based on fuzzy optimization programming technique. Here, binary t-norms are expressed in extended n-ary t-norms and their basic properties and some special cases have been discussed. In this structural model formulation, the objective functions are the weight of the truss and the deflection of loaded joint; the design variables are the cross-sections of the truss members; the constraints are the stresses in members. A classical truss optimization example is presented here to demonstrate the efficiency of our proposed optimization approach. The test problem includes a three-bar planar truss subjected to a single load condition. This approximation approach is used to solve this multi-objective structural optimization model. The model is illustrated with numerical examples. [ABSTRACT FROM AUTHOR]

We present a survey and a comparison of a variety of algorithms that have been proposed over the years to minimize multi-label optimization problems based on the Potts model. Discrete approaches based on Markov Random Fields as well as continuous optimization approaches based on partial differential equations can be applied to the task. In contrast to the case of binary labeling, the multi-label problem is known to be NP hard and thus one can only expect near-optimal solutions. In this paper, we carry out a theoretical comparison and an experimental analysis of existing approaches with respect to accuracy, optimality and runtime, aimed at bringing out the advantages and short-comings of the respective algorithms. Systematic quantitative comparison is done on the Graz interactive image segmentation benchmark. This paper thereby generalizes a previous experimental comparison (Klodt et al. ) from the binary to the multi-label case. [ABSTRACT FROM AUTHOR]

The execution time of many scientific computing applications is dominated by the time spent in performing sparse matrix vector multiplication (SMV; y ← A · x). We consider improving the performance of SMV on multicores by exploiting the dense substructures that are inherently present in many sparse matrices derived from partial differential equation models. First, we identify indistinguishable vertices, i.e., vertices with the same adjacency structure, in a graph representation of the sparse matrix (A) and group them into a supernode. Next, we identify effectively dense blocks within the matrix by grouping rows and columns in each supernode. Finally, by using a suitable data structure for this representation of the matrix, we reduce the number of load operations during SMV while exactly preserving the original sparsity structure of A. In addition, we use ordering techniques to enhance locality in accesses to the vector, x, to yield an SMV kernel that exploits the effectively dense substructures in the matrix. We evaluate our scheme on Intel Nehalem and AMD Shanghai processors. We observe that for larger matrices on the Intel Nehalem processor, our method improves performance on average by 37.35% compared with the traditional compressed sparse row scheme (a blocked compressed form improves performance on average by 30.27%). Benefits of our new format are similar for the AMD processor. More importantly, if we pick for each matrix the best among our method and the blocked compressed scheme, the average performance improvements increase to 40.85%. Additional results indicate that the best performing scheme varies depending on the matrix and the system. We therefore propose an effective density measure that could be used for method selection, thus adding to the variety of options for an auto-tuned optimized SMV kernel that can exploit sparse matrix properties and hardware attributes for high performance. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient method implemented on Graphics Processing Unit (GPU), DEMCMC-GPU, for multi-objective continuous optimization problems. The DEMCMC-GPU kernel is the DEMCMC algorithm, which combines the attractive features of Differential Evolution (DE) and Markov Chain Monte Carlo (MCMC) to evolve a population of Markov chains toward a diversified set of solutions at the Pareto optimal front in the multi-objective search space. With parallel evolution of a population of Markov chains, the DEMCMC algorithm is a natural fit for the GPU architecture. The implementation of DEMCMC-GPU on the pre-Fermi architecture can lead to a 25 speedup on a set of multi-objective benchmark function problems, compare to the CPU-only implementation of DEMCMC. By taking advantage of new cache mechanism in the emerging NVIDIA Fermi GPU architecture, efficient sorting algorithm on GPU, and efficient parallel pseudorandom number generators, the speedup of DEMCMC-GPU can be aggressively improved to 100. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the distributed techniques for optimization problems, the use of ant-based algorithm for mapping Virtual Machines to Cloud platform servers, and the optimization technique particle swarm optimization (PSO).

The Q method of semidefinite programming, developed by Alizadeh, Haeberly and Overton, is extended to optimization problems over symmetric cones. At each iteration of the Q method, eigenvalues and Jordan frames of decision variables are updated using Newton's method. We give an interior point and a pure Newton's method based on the Q method. In another paper, the authors have shown that the Q method for second-order cone programming is accurate. The Q method has also been used to develop a 'warm-starting' approach for second-order cone programming. The machinery of Euclidean Jordan algebra, certain subgroups of the automorphism group of symmetric cones, and the exponential map is used in the development of the Newton method. Finally we prove that in the presence of certain non-degeneracies the Jacobian of the Newton system is nonsingular at the optimum. Hence the Q method for symmetric cone programming is accurate and can be used to 'warm-start' a slightly perturbed symmetric cone program. [ABSTRACT FROM AUTHOR]

Abstract: We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function. [Copyright &y& Elsevier]

A new approach is proposed for shape optimization of shells, where the requirements on geometrical properties, constructability, and mechanical performance are simultaneously considered in the problem formulation. The surface shape is modeled using a triangular Bézier patch to reduce the number of design variables, while the ability of generating moderately complex shape is maintained. The strain energy under specified loading condition is used to represent the mechanical performance, and the geometrical properties are quantitatively defined using algebraic invariants of the parametric surface. The developable surface that has high constructability is generated by assigning an appropriate constraint on an algebraic invariant. The effectiveness of the proposed approach is demonstrated through several numerical examples, and the characteristics of the optimal shapes under various constraints are discussed. [ABSTRACT FROM AUTHOR]

In this correspondence, a two-channel linear phase finite-impulse- response (FIR) quadrature mirror filter (QMF) bank minimax design problem is formulated as a nonconvex optimization problem so that a weighted sum of the maximum amplitude distortion of the filter bank, the maximum passband ripple magnitude and the maximum stopband ripple magnitude of the prototype filter is minimized subject to specifications on these performances. A modified filled function method is proposed for finding the global minimum of the nonconvex optimization problem. Computer numerical simulations show that our proposed design method is efficient and effective. [ABSTRACT FROM AUTHOR]

We provide an improved FPTAS for multiobjective shortest paths—a fundamental (NP-hard) problem in multiobjective optimization—along with a new generic method for obtaining FPTAS to any multiobjective optimization problem with non-linear objectives. We show how these results can be used to obtain better approximate solutions to three related problems, multiobjective constrained [optimal] path and non-additive shortest path, that have important applications in QoS routing and in traffic optimization. We also show how to obtain a FPTAS to a natural generalization of the weighted multicommodity flow problem with elastic demands and values that models several realistic scenarios in transportation and communication networks. [ABSTRACT FROM AUTHOR]

It is commonly believed that diversity is crucial for an evolutionary system to succeed, especially when the problem to be solved contains local optima from which the population cannot easily escape. There exist numerous methods to measure population diversity, but none of these have been shown to be consistently useful. In this paper, a new diversity measure is introduced, and it is shown that high diversity according to this new measure generally leads to a more successful overall evolution in most of the cases considered. [ABSTRACT FROM AUTHOR]

A case-based distance model to solve sorting problems in multiple-criteria decision analysis (MCDA) is developed, and its application in water resources management is presented. The sorting problem in MCDA is to arrange a set of alternatives into ordered groups. MCDA is introduced as consequence-based preference aggregation, whereby consequence and preference expressions (values and weights) are defined and combined in a sequence of steps. Then, sorting problems are defined, and some properties are explained. Based on weighted Euclidean distance, two case-based distance models are developed for sorting using weights and group thresholds obtained by assessment of a case set provided by a decision maker (DM). This case-based method can elicit the DM's preferences more expeditiously and accurately than direct inquiry. Case-based sorting model I is designed for cardinal criteria, while its extension, i.e., case-based sorting model II, can handle both cardinal and ordinal criteria. Optimization programs are employed to find the most descriptive weights and group thresholds. A case study in which Canadian municipalities are sorted according to water usage is presented. [ABSTRACT FROM AUTHOR]

This article offers some solutions used in the assembly of the computerized Uniform Certified Public Accountancy (CPA) licensing examination as practical alternatives for operational programs producing large numbers of forms. The Uniform CPA examination was offered as an adaptive multistage test (MST) beginning in April of 2004. Examples of automated assembly using mixed integer programming (MIP) solutions in Optimization Programming Language software (OPL Studio 3.6.1©, ILOG Inc., Gentilly, France) illustrate MST design features and implementation strategies that can be generalized to other testing programs. This article affords a compromise between the best possible combination of test content and sustainability and security over time by linear programming techniques that make use of mixed integer optimization algorithms. Relative optimization functions are formulated to ensure a variety of constraints are always met for "testlets" of differing difficulties to form panels for the MST design. Technical information is discussed to assist other practitioners and researchers with test assembly optimization problems. Some practical consequences from the selection of statistical targets on testlet exposure are illustrated. These results are described with respect to the fundamental principles of automated test assembly and continuous administration based on MST when testlet or item exposure and inventory rotation are critical concerns. [ABSTRACT FROM AUTHOR]

In recent years, it has been increasingly recognized that constraint and integer programming have orthogonal and complementary strengths in stating and solving combinatorial optimization applications. In addition, their integration has become an active research topic. The optimization programming language OPL was a first attempt at integrating these technologies both at the language and at the solver levels. In particular, OPL is a modeling language integrating the rich language of constraint programming and the ability to specify search procedures at a high level of abstraction. Its implementation includes both constraint and mathematical programming solvers, as well as some cooperation schemes to make them collaborate on a given problem. The purpose of this paper is to illustrate, using OPL, the constraint-programming approach to combinatorial optimization and the complementary strengths of constraint and integer programming. [ABSTRACT FROM AUTHOR]

Angle position sensors (APSs) usually require initial calibration to improve their accuracy. This article introduces a novel offline self-calibration scheme in which a signal flow network is employed to reduce the amplitude errors, direct-current (DC) offsets, and phase shift without requiring extra calibration instruments. In this approach, a signal flow network is firstly constructed to overcome the parametric coupling caused by the linearization model and to ensure the independence of the parameters. The model parameters are stored in the nodes of the network, and the intermediate variables are input into the optimization pipeline to overcome the local optimization problem. A deep learning algorithm is also used to improve the accuracy and speed of convergence to a global optimal solution. The results of simulations show that the proposed method can achieve a high identification accuracy with a relative parameter identification error less than 0.001‰. The practical effects were also verified by implementing the developed technique in a capacitive APS, and the experimental results demonstrate that the sensor error after signal calibration could be reduced to only 6.98%. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: We do not yet know how best to design, implement, and use health information technology (IT). A comprehensive framework that captures knowledge on the implementation, use, and optimization of health IT will help guide more effective approaches in the future. Methods: The authors conducted a targeted review of existing literature on health IT implementation and use, including health IT-related theories and models. By crosswalking elements of current theories and models, the authors identified five major facets of an organizational framework that provides a structure to organize and capture information on the implementation and use of health IT. Results: The authors propose a novel organizational framework for health IT implementation and use with five major facets: technology, use, environment, outcomes, and temporality. Each major facet is described in detail along with associated categories and measures. Conclusion: The proposed framework is an essential first step toward ensuring a more consistent and comprehensive understanding of health IT implementation and use and a more rigorous approach to data collection, measurement development, and theory building. [Copyright &y& Elsevier]

Constraint programming (CP) is a young but rapidly developing technology that supports the modeling and solution of a wide range of planning, scheduling, search, and optimization problems. The integration of CP concepts into languages from other paradigms yields constraint-based multiparadigm programming. This article briefly introduces the constraint-based paradigm and discusses methods, problems, gains, and experiences in integrating CP techniques into the object-oriented paradigm, using Java as an example. [ABSTRACT FROM PUBLISHER]

The article presents a perspective on the ISI Impact Factor of the journal "Journal of Computing." It informs that journal has achieved an over 1.5 which ranks third among the 56 "OR/MS" journals, second among the 6 INFORMS journals. It announces that the former "Logic, Optimization, and Constraint Programming" area of the journal has been altered by area editor John N. Hooker Jr.