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This paper presents improved architectures for a fused floating-point three-term adder. The fused floating-point three-term adder performs two additions in a single unit to achieve better performance and better accuracy compared to a network of traditional floating-point two-term adders, which is referred to as a discrete design. In order to further improve the performance of the three-term adder, several optimization techniques are applied including a new exponent compare and significand alignment, dual-reduction, early normalization, three-input leading zero anticipation, compound addition/rounding and pipelining. The proposed design is implemented for both single and double precision and synthesized with a 45 nm CMOS standard-cell library. The improved fused floating-point three-term adder reduces the area and power consumption by about 20% and reduces the latency by about 35% compared to a discrete floating-point three-term adder. Based on the data flow analysis, the proposed three-term adder can be split into three pipeline stages. Since the latencies of three pipeline stages are fairly well balanced, the throughput is increased to 2.7 times that of the non-pipelined design. [ABSTRACT FROM AUTHOR]

This paper addresses a general method for parameter identification in distributed parameter systems (DPSs) based on a reference model and measurement information. Moreover, the developed identification method is used to deal with secure communication problems. Firstly, in combination with the historical information measured by sensors, an iterative approach based on an approximator is put forward. Meanwhile, an execution scheme for the iterative approach is provided by an optimization policy. Subsequently, by bringing new mechanisms into quantum particle swarm optimization (QPSO) algorithm to enhance its search capability, we propose quantum-leading-following-based optimization (QLFBO) algorithm, which serves the optimization policy. Further, the global convergence for QLFBO algorithm is proved combined with the parameter identification problem of DPSs, and the computational complexity on this algorithm is discussed in detail. Finally, simulation experiments on the diffusion-reaction model are carried out by the iterative approach based on QLFBO algorithm under different measurement ways. Simultaneously, secure communication depending on this model is performed in the double-scroll Chua’s circuit and a lotus image via the investigated iterative strategy and encryption-decryption scheme. Simulation results verify the validity and practicability of the developed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose an event-based sampling policy to implement a constraint-tightening, robust MPC method. The proposed policy enjoys a computationally tractable design and is applicable to perturbed, linear time-invariant systems with polytopic constraints. In particular, the triggering mechanism is suitable for plants with no centralized sensory node as the triggering mechanism can be evaluated locally at each individual sensor. From a geometrical viewpoint, the mechanism is a sequence of hyperrectangles surrounding the optimal state trajectory such that robust recursive feasibility and robust stability are guaranteed. The design of the triggering mechanism is cast as a constrained parametric-in-set optimization problem with the volume of the set as the objective function. Reparameterized in terms of the set vertices, we show that the problem admits a finite tractable convex program reformulation and a linear program relaxation. Several numerical examples are presented to demonstrate the effectiveness and limitations of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper deals with iterative Jacobian-based recursion technique for the root-finding problem of the vector-valued function, whose evaluations are contaminated by noise. Instead of a scalar step size, we use an iterate-dependent matrix gain to effectively weigh the different elements associated with the noisy observations. The analytical development of the matrix gain is built on an iterative-dependent linear function interfered by additive zero-mean white noise, where the dimension of the function is $ {M\ge 1}$ and the dimension of the unknown variable is $ {N\ge 1}$. Necessary and sufficient conditions for $ {M\ge N}$ algorithms are presented pertaining to algorithm stability and convergence of the estimate error covariance matrix. Two algorithms are proposed: one for the case where $ {M\ge N}$ and the second one for the antithesis. The two algorithms assume full knowledge of the Jacobian. The recursive algorithms are proposed for generating the optimal iterative-dependent matrix gain. The proposed algorithms here aim for per-iteration minimization of the mean square estimate error. We show that the proposed algorithm satisfies the presented conditions for stability and convergence of the covariance. In addition, the convergence rate of the estimation error covariance is shown to be inversely proportional to the number of iterations. For the antithesis $ {M< N}$ , contraction of the error covariance is guaranteed. This underdetermined system of equations can be helpful in training neural networks. Numerical examples are presented to illustrate the performance capabilities of the proposed multidimensional gain while considering nonlinear functions. [ABSTRACT FROM AUTHOR]

This is a companion paper of [Mixed equilibrium solution of time-inconsistent stochastic linear-quadratic problem, SIAM J. Control Optim., vol. 57, no. 1, 533–569, 2019], where general theory has been established to characterize the open-loop equilibrium control, feedback equilibrium strategy and mixed equilibrium solution for a time-inconsistent stochastic linear-quadratic problem. This note is, on the one hand, to test the developed theory of that paper and on the other hand to push the solvability of multiperiod mean-variance portfolio selection. A nondegenerate assumption, which is popular in the existing literature about multiperiod mean-variance portfolio selection, has been removed in this note; and neat conditions have been obtained to characterize the existence of equilibrium solutions. [ABSTRACT FROM AUTHOR]

This paper considers an unconstrained collaborative optimization of a sum of convex functions, where agents make decisions using local information in the presence of random interconnection topologies. We recast the problem as minimization of the sum of convex functions over a constraint set defined as the set of fixed-value points of a random operator derived from weighted matrices of random graphs. We show that the derived random operator has nonexpansivity property; therefore, this formulation does not need the distribution of random communication topologies. Hence, it includes random networks with/without asynchronous protocols. As an extension of the problem, we define a novel optimization problem, namely minimization of a convex function over the fixed-value point set of a nonexpansive random operator. We propose a discrete-time algorithm using diminishing step size for converging almost surely and in mean square to the global solution of the optimization problem under suitable assumptions. Consequently, as a special case, it reduces to a totally asynchronous algorithm for the distributed optimization problem. We show that fixed-value point is a bridge from deterministic analysis to random analysis of the algorithm. Finally, a numerical example illustrates the convergence of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes scalable, distributed algorithms for solving linear equations by integrating two mechanisms, termed consensus and conservation, in double-layered multiagent networks. The multiagent network considered in this paper is composed of clusters and each cluster consists of an aggregator and a subnetwork of agents. By achieving consensus and conservation through agent–agent communications in the same cluster and aggregator–aggregator communications among different clusters, respectively, distributed algorithms are devised for agents to cooperatively achieve a solution to the overall linear equation. These algorithms outperform existing algorithms, including but not limited to the following aspects—first, each agent does not have to know as much as a complete row or column of the overall equation; second, each agent only needs to control as few as two scalar states when the number of clusters and the number of agents are sufficiently large; third, the dimensions of agents’ states in the proposed algorithms do not have to be the same (while in contrast, algorithms based on the idea of standard consensus inherently require all agents’ states to be of the same dimension). Both analytical proof and simulation results are provided to validate exponential convergence of the proposed distributed algorithms in solving linear equations. [ABSTRACT FROM AUTHOR]

This paper proposes a quaternion-valued one-layer recurrent neural network approach to resolve constrained convex function optimization problems with quaternion variables. Leveraging the novel generalized Hamilton-real (GHR) calculus, the quaternion gradient-based optimization techniques are proposed to derive the optimization algorithms in the quaternion field directly rather than the methods of decomposing the optimization problems into the complex domain or the real domain. Via chain rules and Lyapunov theorem, the rigorous analysis shows that the deliberately designed quaternion-valued one-layer recurrent neural network stabilizes the system dynamics while the states reach the feasible region in finite time and converges to the optimal solution of the considered constrained convex optimization problems finally. Numerical simulations verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust event-triggered model predictive control (MPC) of discrete-time constrained linear systems subject to bounded additive disturbances. We make use of the interpolation technique to construct a feedback policy and tighten the original system constraint accordingly to fulfill robust constraint satisfaction. A dynamic event trigger that allows the controller to solve the optimization problem only at triggering time instants is developed, where the triggering threshold is related to the interpolating coefficient of the feedback policy and determined via optimization. We show that the proposed algorithm is recursively feasible and the closed-loop system is input-to-state stable in the attraction region. Finally, a numerical example is provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

The explicit reference governor (ERG) is a simple and systematic approach that provides constraint handling capabilities to prestabilized systems. The basic idea behind this approach is to translate state and input constraints into an upper-bound on the value of the Lyapunov function, which is then enforced by suitably manipulating the derivative of the applied reference. When designing the ERG, one of the main challenges is the determination of an upper-bound on the value of the Lyapunov function that ensures constraints satisfaction. This paper proposes a systematic approach for estimating online the optimal upper-bound for systems subject to the intersection of concave constraints. To do this, the Barrier function method is used. The effect of the estimation error caused by the time-varying nature of the auxiliary reference on the constraint satisfaction capability of the ERG is studied analytically. A procedure is proposed to modify the estimated upper-bound to avoid constraints violation in the presence of estimation errors. The effectiveness of the proposed scheme is demonstrated through a simulation study on an overhead gantry crane system. [ABSTRACT FROM AUTHOR]

Average consensus underpins key functionalities of distributed systems ranging from distributed information fusion, decision-making, distributed optimization, to load balancing and decentralized control. Existing distributed average consensus algorithms require each node to exchange and disclose state information to its neighbors, which is undesirable in cases where the state is private or contains sensitive information. In this paper, we propose a novel approach that avoids disclosing individual state information in average consensus by letting each node decompose its state into 2 substates. For each node, one of the two substates involves in computation and internode interactions as if it were the original state, while the other substate interacts only with the first substate of the same node, being completely invisible to other nodes. The initial values of the two substates are chosen randomly but with their mean fixed to the initial value of the original state, which is key to guarantee convergence to the desired consensus value. In direct contrast to differential-privacy based privacy-preserving average-consensus approaches, which enable privacy by compromising accuracy in the consensus value, the proposed approach can guarantee convergence to the exact desired value without any error. Not only is the proposed approach able to prevent the disclosure of a node's initial state to honest-but-curious neighbors, it can also provide protection against inference by external eavesdroppers able to wiretap communication links. Numerical simulations demonstrate the effectiveness of the approach and its advantages over state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

This paper aims to reduce the communication and computation costs of the Nash equilibrium seeking strategy for the $N$ -coalition noncooperative games. The objective is achieved by the following two manners: 1) an interference graph is introduced to describe the interactions among the agents in each coalition and 2) the Nash equilibrium seeking strategy is designed with the interference graphs considered. The convergence property of the proposed Nash equilibrium seeking strategy is analytically investigated. It is shown that the agents’ actions generated by the proposed method converge to a neighborhood of the Nash equilibrium of the graphical $N$ -coalition noncooperative games under certain conditions. Several special cases where there is only one coalition and/or there are coalitions with only one agent are considered. The results for the special cases demonstrate that the proposed seeking strategy achieves the solution seeking for noncooperative games, social cost minimization problems, and single-agent optimization problems in a unified framework. Numerical examples are presented to support the theoretical results. [ABSTRACT FROM AUTHOR]

We consider random walks in which the walk originates in one set of nodes and then continues until it reaches one or more nodes in a target set. The time required for the walk to reach the target set is of interest in understanding the convergence of Markov processes, as well as applications in control, machine learning, and social sciences. In this paper, we investigate the computational structure of the random walk times as a function of the set of target nodes, and find that the commute, hitting, and cover times all exhibit submodular structure, even in nonstationary random walks. We provide a unifying proof of this structure by considering each of these times as special cases of stopping times. We generalize our framework to Markov decision processes, in which the target sets and control policies are jointly chosen to minimize the travel times, leading to polynomial-time approximation algorithms for choosing target sets. Our results are validated through numerical study. [ABSTRACT FROM AUTHOR]

Many machine learning problems involve learning a low-rank positive semidefinite matrix. However, existing solvers for this low-rank semidefinite program (SDP) are often expensive. In this paper, by factorizing the target matrix as a product of two matrices and using a Courant penalty to penalize for their difference, we reformulate the SDP as a biconvex optimization problem. This allows the use of multiconvex optimization techniques to define simple surrogates, which can be minimized easily by block coordinate descent. Moreover, while traditionally this biconvex problem approaches the original problem only when the penalty parameter is infinite, we show that the two problems are equivalent when the penalty parameter is sufficiently large. Experiments on a number of SDP applications in machine learning show that the proposed algorithm is as accurate as other state-of-the-art algorithms, but is much faster, especially on large data sets. [ABSTRACT FROM AUTHOR]

Design of hardware efficient digital systems has drawn appreciable attention of researchers over the last few decades. To this aim, this paper considers the design of a narrow transition band finite impulse response (FIR) filter operated in an interpolated bandpass filtering approach. An optimization problem is formulated that minimizes the complexity measure of the narrow transition band FIR filter under the constraints of length of the filter and normalized peak ripple magnitude (NPRM) threshold. Closed-form expressions for the optimal interpolation factor as well as the passband and stopband edges to obtain the narrow transition band are derived. A designed filter is subsequently implemented on Altera’s Cyclone IV field programmable gate array (FPGA) chip and supremacy of the suggested design has strongly been established by correlating its hardware cost with many of the state-of-the-art FIR filters. [ABSTRACT FROM AUTHOR]

Recently, symmetric block-wise concatenated-BCH (SBC-BCH) codes are proposed as strong error-correcting codes (ECCs) based on hard-decision channel outputs, which is especially suited for storages using NAND flash memories. Targeting energy-efficient NAND flash memory applications, this paper presents an energy-optimized decoder architecture which includes an iterative decoder for a SBC-BCH code as a main decoder and a low-complexity auxiliary decoder for a block-wise single parity-check (BSPC) code. The auxiliary decoder is opportunistically in action to break the dominant error bound associated with the SBC-BCH code, which allows one to lower the uncorrectable bit-error-rate (UBER) to 10−15 in an energy efficient way. This work presents several design-level optimizations for further enhancing the energy-efficiency of the iterative SBC-BCH decoder. More precisely, the new initialization scheme is proposed for ensuring the energy-efficient seamless decoding scenario. The syndrome tracking is applied to eliminate the previous syndrome calculation and the reordered Chien search further enhances the energy-efficiency as well as the decoding throughput. Targeting a 0.9-rate 4KB SBC-BCH code for commercialized storages using NAND flash memories, a prototype decoder consisting of both the iterative main and auxiliary decoders is designed in a 65-nm CMOS process. By applying the proposed optimizations, the prototype decoder achieves an energy-efficiency of 3.43 pJ/b while providing a decoding throughput of 13.2 Gb/s, which is superior to the previous state-of-the-art decoders for mobile storages. [ABSTRACT FROM AUTHOR]

Recently, there is a rapidly increasing attraction for the efficient recovery of low-rank matrix in computer vision and machine learning. The popular convex solution of rank minimization is nuclear norm-based minimization (NNM), which usually leads to a biased solution since NNM tends to overshrink the rank components and treats each rank component equally. To address this issue, some nonconvex nonsmooth rank (NNR) relaxations have been exploited widely. Different from these convex and nonconvex rank substitutes, this paper first introduces a general and flexible rank relaxation function named weighted NNR relaxation function, which is actually derived from the initial double NNR (DNNR) relaxations, i.e., DNNR relaxation function acts on the nonconvex singular values function (SVF). An iteratively reweighted SVF optimization algorithm with continuation technology through computing the supergradient values to define the weighting vector is devised to solve the DNNR minimization problem, and the closed-form solution of the subproblem can be efficiently obtained by a general proximal operator, in which each element of the desired weighting vector usually satisfies the nondecreasing order. We next prove that the objective function values decrease monotonically, and any limit point of the generated subsequence is a critical point. Combining the Kurdyka–Łojasiewicz property with some milder assumptions, we further give its global convergence guarantee. As an application in the matrix completion problem, experimental results on both synthetic data and real-world data can show that our methods are competitive with several state-of-the-art convex and nonconvex matrix completion methods. [ABSTRACT FROM AUTHOR]

For many specific applications in data mining and machine learning, we face explicit or latent size constraint for each cluster that leads to the “balanced clustering” problem. Many existing clustering algorithms perform well in partitioning but fail in producing balanced clusters and preserving the naturally balanced structure of some data. In this paper, we propose a novel balanced clustering framework that flexibly utilizes local and global information of data. First, we propose the global balanced clustering (GBC), in which a global discriminative partitioning model is combined with the minimization of the distribution entropy of data. Then, we show that the proposed GBC can be further used to globally regularize some widely used local clustering models, so as to transform them into balanced clustering that simultaneously capture local and global data. We apply our global balanced regularization to spectral clustering (SC) and local learning (LL)-based clustering, respectively, and propose another two novel balanced clustering models: the local and global balanced SC (LGB-SC) and LGB-LL. Finding the optimal balanced partition is nondeterministic polynomial-time (NP)-hard in general. We adopt the method of augmented Lagrange multipliers to help optimize our model. Comprehensive experiments on several real world benchmarks demonstrate the advantage of our framework to yield balanced clusters while preserving good clustering quality. Our proposed LGB-SC and LGB-LL also outperform SC and LL as well as other classical clustering methods. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to design multiplierless constant rotators. The approach is based on a combined coefficient selection and shift-and-add implementation (CCSSI) for the design of the rotators. First, complete freedom is given to the selection of the coefficients, i.e., no constraints to the coefficients are set in advance and all the alternatives are taken into account. Second, the shift-and-add implementation uses advanced single constant multiplication (SCM) and multiple constant multiplication (MCM) techniques that lead to low-complexity multiplierless implementations. Third, the design of the rotators is done by a joint optimization of the coefficient selection and shift-and-add implementation. As a result, the CCSSI provides an extended design space that offers a larger number of alternatives with respect to previous works. Furthermore, the design space is explored in a simple and efficient way. The proposed approach has wide applications in numerous hardware scenarios. This includes rotations by single or multiple angles, rotators in single or multiple branches, and different scaling of the outputs. Experimental results for various scenarios are provided. In all of them, the proposed approach achieves significant improvements with respect to state of the art. [ABSTRACT FROM AUTHOR]

Most wireless battery-operated devices spend more energy receiving than transmitting. Hence, minimizing the power dissipation in the receiver front end, which, in many cases, is the prominent power consuming part of the receiver, is an important challenge. This paper addresses this challenge by solving two closely related optimization problems. Firstly, we optimize the overall power dissipation in an RF front end consisting of a chain of building blocks to satisfy required overall specifications in gain, linearity and noise figure. We extend this into a second optimization problem, namely to maximize the transmission rate that the receiver can accommodate for a given available receiver battery power budget. In fact, the ratio of this transmission rate vs the available receiver power budget serves as the figure-of-merit that allows a formal optimization where, in particular, the (adjacent channel) interference is a critical factor. Our results include closed-form analytical solutions for certain cases. For high signal power, where the noise is limited by interference, the largest bit/s/Hz per nJ drawn from receiver is reached for a transmission rate of 2.3 bits/s/Hz, irrespective of interference power. Numerical results using practical circuit blocks with 90 nm and 65 nm technologies are in close agreement with the analytical results. [ABSTRACT FROM AUTHOR]

Recently, there has been a lot of success in the development of effective binary classifiers. Although many statistical classification techniques have natural multiclass extensions, some, such as the support vector machines, do not. The existing techniques for mapping multiclass problems onto a set of simpler binary classification problems run into serious efficiency problems when there are hundreds or even thousands of classes, and these are the scenarios where this paper's contributions shine. We introduce the concept of correlation and joint probability of base binary learners. We learn these properties during the training stage, group the binary leaner's based on their independence and, with a Bayesian approach, combine the results to predict the class of a new instance. Finally, we also discuss two additional strategies: one to reduce the number of required base learners in the multiclass classification, and another to find new base learners that might best complement the existing set. We use these two new procedures iteratively to complement the initial solution and improve the overall performance. This paper has two goals: finding the most discriminative binary classifiers to solve a multiclass problem and keeping up the efficiency, i.e., small number of base learners. We validate and compare the method with a diverse set of methods of the literature in several public available datasets that range from small (10 to 26 classes) to large multiclass problems (1000 classes) always using simple reproducible scenarios. [ABSTRACT FROM PUBLISHER]

Multiple constant multiplication (MCM) scheme is widely used for implementing transposed direct-form FIR filters. While the research focus of MCM has been on more effective common subexpression elimination, the optimization of adder-trees, which sum up the computed sub-expressions for each coefficient, is largely omitted. In this paper, we have identified the resource minimization problem in the scheduling of adder-tree operations for the MCM block, and presented a mixed integer programming (MIP) based algorithm for more efficient MCM-based implementation of FIR filters. Experimental result shows that up to 15% reduction of area and 11.6% reduction of power (with an average of 8.46% and 5.96% respectively) can be achieved on the top of already optimized adder/subtractor network of the MCM block. [ABSTRACT FROM AUTHOR]

This paper is an attempt to study the first passage optimality criterion for continuous-time Markov decision processes with state-dependent discount factors and history-dependent policies. The state space is denumerable, the action space is a Borel space, and the transition and reward rates are unbounded. Under suitable conditions, we show the existence of a deterministic stationary optimal policy, establish the Bellman (optimality) equation, to which the value function is the unique solution, and give the value and policy iteration algorithms for solving (at least approximating) the value function and an optimal policy. Furthermore, we give examples about reliability and controlled birth processes with killing to illustrate the potential applications of the results obtained here, and also to show the difference between the main results in this paper and those in the previous literature. [ABSTRACT FROM PUBLISHER]

A model-based event-triggered control scheme for nonlinear constrained continuous-time uncertain systems in networked configuration is presented in this paper. It is based on the combined use of model-predictive control (MPC) and integral sliding-mode (ISM) control, and it is oriented to reduce the packet transmission over the network both in the direct path and in the feedback path, in order to avoid network congestion. The key elements of the proposed control scheme are the ISM local control law, the MPC remote controller, a smart sensor, and a smart actuator, both containing a copy of the nominal model of the plant. The role of the ISM control law is to compensate matched uncertainties, without amplifying the unmatched ones. The MPC controller with tightened constraints generates the control component oriented to comply with state and control requirements and is asynchronous since the underlying constrained optimization problem is solved only when a triggering event occurs. In the paper, the robustness properties of the controlled system are theoretically analyzed, proving the regional input-to-state practical stability of the overall control scheme. [ABSTRACT FROM AUTHOR]

This paper presents a compact model for threshold self-compensated rectifiers that can be used to optimize circuit parameters early in the design phase instead of time-consuming transient simulations. A design procedure is presented for finding the optimum aspect ratio of transistors used in the converter and number of rectifying stages to achieve the maximum power conversion efficiency. In the presented analysis, the relation between the power conversion efficiency and the load current over the variation of the output dc voltage is used to find the optimum design parameters. Accurate approximation of the charge entering to and exiting from the rectifier during the steady state helps to get good agreement between the predicted output dc voltage and simulation results under different load current values. Using the Enz-Krummenacher-Vittoz model for the MOS transistor, an accurate formula is derived for optimization. The ability of the model in weak, moderate, and strong inversion region aids to find the optimum design parameters for broad range of RF voltage variation. The presented formula for estimating the optimum size and number of stages shows good agreement with simulations. Further validation is provided by comparing model predictions with measurement results of a four-stage self-compensated rectifier operating at 900 MHz, which is implemented in a commercial 0.18- \mu \textm partially-depleted silicon-on-insulator process. [ABSTRACT FROM PUBLISHER]

Energy efficiency is highly desirable for sensing systems in the Internet of Things. A common approach to achieve low-power systems is duty cycling, where components in a system are turned OFF periodically to meet an energy budget. However, this paper shows that such an approach is not necessarily optimal in energy efficiency, and proposes guided processing as a fundamentally better alternative. The proposed approach offers: 1) explicit modeling of performance uncertainties in system internals; 2) a realistic resource consumption model; and 3) a key insight into the superiority of guided processing over duty cycling. Generalization from the cascade structure to the more general graph-based one is also presented. Once applied to optimize a large-scale audio sensing system with a practical detection application, empirical results show that the proposed approach significantly improves the detection performance (up to 1.7 times and 4 times reduction in false alarm and miss rate, respectively) for the same energy consumption, when compared with the duty-cycling approach. [ABSTRACT FROM PUBLISHER]

This paper presents a collective neurodynamic approach with multiple interconnected recurrent neural networks (RNNs) for distributed constrained optimization. The objective function of the distributed optimization problems to be solved is a sum of local convex objective functions, which may be nonsmooth. Subject to its local constraints, each local objective function is minimized individually by using an RNN, with consensus among others. In contrast to existing continuous-time distributed optimization methods, the proposed collective neurodynamic approach is capable of solving more general distributed optimization problems. Simulation results on three numerical examples are discussed to substantiate the effectiveness and characteristics of the proposed approach. In addition, an application to the optimal placement problem is delineated to demonstrate the viability of the approach. [ABSTRACT FROM AUTHOR]

This paper presents a family of robust set-membership normalized subband adaptive filtering (RSM-NSAF) algorithms for acoustic echo cancellation (AEC). By using a new robust set-membership error bound, the RSM-NSAF algorithm obtains improved robustness against impulsive noises and decreased steady-state misalignment relative to the conventional set-membership NSAF (SM-NSAF) algorithm. To exploit the sparsity of the impulse response, the L0 norm constraint robust set-membership NSAF ( L0 -RSM-NSAF), robust set-membership improved proportionate NSAF (RSM-IPNSAF), and L0 norm constraint robust set-membership improved proportionate NSAF ( L0 -RSM-IPNSAF) algorithms are derived by minimizing a differentiable cost function that utilizes the Riemannian distance between the updated and previous weight vectors as well as the L0 norm of the weighted updated weight vector. Simulations in AEC application confirm the improvements of the proposed algorithms in performance. [ABSTRACT FROM PUBLISHER]

Noise shaping filters for quantizers with error feedback are designed to mitigate the effects of quantization errors. In this paper, we prove that if the transfer function from the quantization error to the signal-of-interest has minimum phase and there is no constraint on the feedback signal, then the scaled inverse of the transfer function is the optimal noise shaping filter. Next, we design a noise shaping filter to minimize the variance or the l2 norm of the error in the signal-of-interest under a constraint on the variance or the l2 norm of the feedback signal, which can be expressed as bilinear matrix inequalities (BMIs). Although the BMIs are not convex, we prove that the minimization of the error variance under the constraint on the variance of the feedback signal can be cast into a convex optimization problem. This enables us to design optimal noise shaping filters using numerical methods. We formulate our design problem as a convex optimization problem using extended linear matrix inequalities to obtain noise shaping filters, except for the special case. Examples are provided to demonstrate the effectiveness of the designed noise shaping filters. [ABSTRACT FROM AUTHOR]

Decentralized optimization of distributed stochastic dynamical systems with two or more controls of the decision makers (DMs) has been an active area of research for over half a century. Although, such decentralized optimization problems are often formulated utilizing static team and person-by-person (PbP) optimality criteria, the corresponding static team theory results have not been extended to dynamical systems. In this first part of the two-part paper, we derive team and PbP optimality conditions for distributed stochastic differential systems, when the controls of the DMs generate actions based on different information structures. The necessary conditions are given by a Hamiltonian System described by coupled backward and forward stochastic differential equations (SDEs) and a conditional Hamiltonian, conditioned on the information structures available to the controls of the DMs. The sufficient conditions state that PbP optimality implies team optimality, if the Hamiltonian is convex in the state and/or actions spaces of the controls of the DMs. We show existence of relaxed team optimal strategies, when the information structures are not affected by the controls of the DMs. Throughout the paper we discuss similarities to analogous optimality conditions of centralized decision or control of stochastic systems, and we note a connections to mean field stochastic optimal control problems. [ABSTRACT FROM AUTHOR]

This paper presents a two-time-scale neurodynamic approach to constrained minimax optimization using two coupled neural networks. One of the recurrent neural networks is used for minimizing the objective function and another is used for maximization. It is shown that the coupled neurodynamic systems operating in two different time scales work well for minimax optimization. The effectiveness and characteristics of the proposed approach are illustrated using several examples. Furthermore, the proposed approach is applied for $H_\infty $ model predictive control. [ABSTRACT FROM PUBLISHER]

In this paper, we describe an optimization for binary radix-16 (modified) Booth recoded multipliers to reduce the maximum height of the partial product columns to \lceil n/4\rceil for n=\text{64-bit} unsigned operands. This is in contrast to the conventional maximum height of \lceil(n+1)/4\rceil$. Therefore, a reduction of one unit in the maximum height is achieved. This reduction may add flexibility during the design of the pipelined multiplier to meet the design goals, it may allow further optimizations of the partial product array reduction stage in terms of area/delay/power and/or may allow additional addends to be included in the partial product array without increasing the delay. The method can be extended to Booth recoded radix-8 multipliers, signed multipliers, combined signed/unsigned multipliers, and other values of $n$. [ABSTRACT FROM PUBLISHER]

This paper develops a novel decentralized dimensionality reduction algorithm for the distributed tensor data across sensor networks. The main contributions of this paper are as follows. First, conventional centralized methods, which utilize entire data to simultaneously determine all the vectors of the projection matrix along each tensor mode, are not suitable for the network environment. Here, we relax the simultaneous processing manner into the one-vector-by-one-vector (OVBOV) manner, i.e., determining the projection vectors (PVs) related to each tensor mode one by one. Second, we prove that in the OVBOV manner each PV can be determined without modifying any tensor data, which simplifies corresponding computations. Third, we cast the decentralized PV determination problem as a set of subproblems with consensus constraints, so that it can be solved in the network environment only by local computations and information communications among neighboring nodes. Fourth, we introduce the null space and transform the PV determination problem with complex orthogonality constraints into an equivalent hidden convex one without any orthogonality constraint, which can be solved by the Lagrange multiplier method. Finally, experimental results are given to show that the proposed algorithm is an effective dimensionality reduction scheme for the distributed tensor data across the sensor networks. [ABSTRACT FROM AUTHOR]

Face sketch–photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch–photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch–photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science. [ABSTRACT FROM PUBLISHER]

Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a support vector machine algorithm. Specifically, the goal is to learn a set of classifiers with common direction vectors and different biases correctly separating the ordered classes. Current algorithms are either required to solve a quadratic optimization problem, which is computationally expensive, or based on maximizing the minimum margin (i.e., a fixed-margin strategy) between a set of hyperplanes, which biases the solution to the closest margin. Another drawback of these strategies is that they are limited to order the classes using a single ranking variable (e.g., perceived length). In this paper, we define a multiple ordinal regression algorithm based on maximizing the sum of the margins between every consecutive class with respect to one or more rankings (e.g., perceived length and weight). We provide derivations of an efficient, easy-to-implement iterative solution using a sequential minimal optimization procedure. We demonstrate the accuracy of our solutions in several data sets. In addition, we provide a key application of our algorithms in estimating human subjects’ ordinal classification of attribute associations to object categories. We show that these ordinal associations perform better than the binary one typically employed in the literature. [ABSTRACT FROM AUTHOR]

Most of the research on the implementation of finite impulse response (FIR) filter so far focuses on the optimization of the multiple constant multiplication (MCM) block. But it is observed that the product-accumulation section often contributes the major part of the critical path, such that the timing optimization of MCM block does not impact significantly on the overall speedup of the FIR filters. In this paper, a precise analysis and optimization of critical path for transposed direct from (TDF) FIR filters is proposed. The delay increment introduced by structural adders is estimated by comparing the delay of a tap and the corresponding delay of the coefficient multiplication only. Based on that, a novel implementation of the product-accumulation section of FIR filters is proposed by retiming the existing delays into the structural adders. It is also shown that the structural adders can be integrated with the MCM block and retimed together for further reduction of critical path. By using the proposed method, the increment of delay caused by the structural adders can be either completely eliminated or significantly reduced. Experimental results show that the critical path delay can be significantly reduced at the cost of very small area overhead. The overall area-delay performance and power-delay performance of the proposed method are superior to the existing methods. [ABSTRACT FROM PUBLISHER]

Element matching approaches based on Optimal Combination Algorithms (OCAs) are methods that try to calibrate element mismatches in data converters by rearranging the mapping relations between circuit elements and control logics. This work for the first time proposes a universal method and its corresponding Figure-of-Merit to evaluate the performance of various OCA approaches. It adopts the quantile of integral nonlinearity as the criterion and is consequently compatible with most of matching OCAs. Dynamic performance and the corresponding effective-number-of-bits (ENOB) limited by mismatches are also investigated. A critical feature of the proposed method is its robustness to the disturbance of element standard deviation as long as the standard deviation is in a reasonable region. Merits of our method have been explained and proved mathematically, and also been verified numerically by data extracted from previously reported works and their reproduction by authors, which together make this proposal more convincing and rigorous. Due to those merits, our proposed method could also help designers to specify a feasible standard deviation for circuit elements when they are designing data converters with OCAs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a neural network modeled by a differential inclusion to solve a class of nonsmooth convex optimization problems, where the constraints are defined by a class of nonsmooth convex inequality constraints and a class of affine equality constraints. For any initial point, the solution of the proposed network is global existent, unique and uniformly bounded, which is just the “slow solution” of network. By the regularization item, without any estimation on the exact penalty parameters, the solution of proposed network is convergent to the optimal solution set of optimization problem. Moreover, when the feasible region satisfies another condition, the solution of proposed network converges to the feasible region in finite time and to the particular element in the optimal solution set with the smallest norm, which indicates that our proposed neural network is globally attractive. Some numerical examples are presented to illustrate the effectiveness of the proposed neural network for solving nonsmooth convex optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, we consider Newton–Kantorovich type methods for solving control-constrained optimal control problems that appear in model predictive control. Conditions for convergence are established for an inexact version of the Newton–Kantorovich method applied to variational inequalities. Based on these results, two groups of algorithms are proposed to solve the optimality system. The first group includes exact and inexact Newton and Newton–Kantorovich implementations of the sequential quadratic programming. In the second group, exact and inexact Newton and Newton–Kantorovich methods are developed for solving a nonsmooth normal map equation equivalent to the optimality system. Numerical simulations featuring examples from the aerospace and automotive domain are presented, which show that inexact Newton–Kantorovich type methods can achieve significant reduction of the computational time. [ABSTRACT FROM AUTHOR]

The recent studies have found that the nonconvex relaxation functions usually perform better than the convex counterparts in the $l_{0}$ -norm and rank function minimization problems. However, due to the absence of convexity in these nonconvex problems, developing efficient algorithms with convergence guarantee becomes very challenging. Inspired by the basic ideas of both the Jacobian alternating direction method of multipliers (JADMMs) for solving linearly constrained problems with separable objectives and the proximal gradient methods (PGMs) for optimizing the unconstrained problems with one variable, this paper focuses on extending the PGMs to the proximal Jacobian iteration methods (PJIMs) for handling with a family of nonconvex composite optimization problems with two splitting variables. To reduce the total computational complexity by decreasing the number of iterations, we devise the accelerated version of PJIMs through the well-known Nesterov’s acceleration strategy and further extend both to solve the multivariable cases. Most importantly, we provide a rigorous convergence analysis, in theory, to show that the generated variable sequence globally converges to a critical point by exploiting the Kurdyka–Łojasiewica (KŁ) property for a broad class of functions. Furthermore, we also establish the linear and sublinear convergence rates of the obtained variable sequence in the objective function. As the specific application to the nonconvex sparse and low-rank recovery problems, several numerical experiments can verify that the newly proposed algorithms not only keep fast convergence speed but also have high precision. [ABSTRACT FROM AUTHOR]

A fundamental challenge in multiagent systems is to design local control algorithms to ensure a desirable collective behavior. The information available to the agents, gathered either through communication or sensing, naturally restricts the achievable performance. Hence, it is fundamental to identify what piece of information is valuable and can be exploited to design control laws with enhanced performance guarantees. This paper studies the case when such information is uncertain or inaccessible for a class of submodular resource allocation problems termed covering problems. In the first part of this paper, we pinpoint a fundamental risk-reward tradeoff faced by the system operator when conditioning the control design on a valuable but uncertain piece of information, which we refer to as the cardinality, that represents the maximum number of agents that can simultaneously select any given resource. Building on this analysis, we propose a distributed algorithm that allows agents to learn the cardinality while adjusting their behavior over time. This algorithm is proved to perform on par or better to the optimal design obtained when the exact cardinality is known a priori. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the fuzzy constrained matrix game (MG) problems using the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first in attempting to find a solution for fuzzy game problems using RNN models. For this purpose, a fuzzy game problem is reformulated into a weighting problem. Then, the Karush–Kuhn–Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Moreover, the Lyapunov stability and the global convergence of the RNN model are also confirmed. Finally, three illustrative examples are presented to demonstrate the effectiveness of this approach. The obtained results are compared with the results obtained by the previous approaches for solving fuzzy constrained MG. [ABSTRACT FROM AUTHOR]

This paper presents a two-timescale duplex neurodynamic system for constrained biconvex optimization. The two-timescale duplex neurodynamic system consists of two recurrent neural networks (RNNs) operating collaboratively at two timescales. By operating on two timescales, RNNs are able to avoid instability. In addition, based on the convergent states of the two RNNs, particle swarm optimization is used to optimize initial states of the RNNs to avoid local minima. It is proven that the proposed system is globally convergent to the global optimum with probability one. The performance of the two-timescale duplex neurodynamic system is substantiated based on the benchmark problems. Furthermore, the proposed system is applied for $L_{1}$ -constrained nonnegative matrix factorization. [ABSTRACT FROM AUTHOR]

Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data during the past several years. However, they are unable to construct the state-of-the-art CNNs due to their intrinsic architectures. In this regard, we propose a flexible CAE (FCAE) by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional CAE. We also design an architecture discovery method by exploiting particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed FCAE with much less computational resource and without any manual intervention. We test the proposed approach on four extensively used image classification data sets. Experimental results show that our proposed approach in this paper significantly outperforms the peer competitors including the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Unsupervised feature selection is fundamentally important for processing unlabeled high-dimensional data, and several methods have been proposed on this topic. Most existing embedded unsupervised methods just emphasize the data structure in the input space, which may contain large noise. Therefore, they are limited to perceive the discriminative information implied within the low-dimensional manifold. In addition, these methods always involve several parameters to be tuned, which is time-consuming. In this paper, we present a self-tuned discrimination-aware (STDA) approach for unsupervised feature selection. The main contributions of this paper are threefold: 1) it adopts the advantage of discriminant analysis technique to select the valuable features; 2) it learns the local data structure adaptively in the discriminative subspace to alleviate the effect of data noise; and 3) it performs feature selection and clustering simultaneously with an efficient optimization strategy, and saves the additional efforts to tune parameters. Experimental results on a toy data set and various real-world benchmarks justify the effectiveness of STDA on both feature selection and data clustering, and demonstrate its promising performance against the state of the arts. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is one of the most widely spread advanced control schemes in industry today. In MPC, a constrained finite-time optimal control (CFTOC) problem is solved at each iteration in the control loop. The CFTOC problem can be solved using, for example, second-order methods, such as interior-point or active-set methods, where the computationally most demanding part often consists of computing the sequence of second-order search directions. Each search direction can be computed by solving a set of linear equations that corresponds to solving an unconstrained finite-time optimal control (UFTOC) problem. In this paper, different direct (noniterative) parallel algorithms for solving UFTOC problems are presented. The parallel algorithms are all based on a recursive variable elimination and solution propagation technique. Numerical evaluations of one of the parallel algorithms indicate that a significant boost in performance can be obtained, which can facilitate high-performance second-order MPC solvers. [ABSTRACT FROM AUTHOR]

This paper considers solving convex quadratic programs in a real-time setting using a regularized and smoothed Fischer–Burmeister method (FBRS). The Fischer–Burmeister function is used to map the optimality conditions of a quadratic program to a nonlinear system of equations which is solved using Newton's method. Regularization and smoothing are applied to improve the practical performance of the algorithm and a merit function is used to globalize convergence. FBRS is simple to code, easy to warmstart, robust to early termination, and has attractive theoretical properties, making it appealing for real time and embedded applications. Numerical experiments using several predictive control examples show that the proposed method is competitive with other state-of-the-art solvers. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are powerful machine learning models that have become the state of the art in several problems in the areas of computer vision and image processing. Nevertheless, the knowledge of why and how these models present an impressive performance is still limited. There are visualization techniques that can help us to understand the inner working of neural networks. However, they have mostly been applied to classification models. In this paper, we evaluate the application of visualization methods to networks where the input and output are images of proportional dimensions. The results show that visualization brings visual cues associated with how these systems work, helping in their understanding and improvement. We use the knowledge obtained from the visualization of an image restoration CNN to improve the architecture’s efficiency with no significant degradation of its performance. [ABSTRACT FROM AUTHOR]

Subspace clustering is a problem of exploring the low-dimensional subspaces of high-dimensional data. State-of-the-art approaches are designed by following the model of spectral clustering-based method. These methods pay much attention to learn the representation matrix to construct a suitable similarity matrix and overlook the influence of the noise term on subspace clustering. However, the real data are always contaminated by the noise and the noise usually has a complicated statistical distribution. To alleviate this problem, in this paper, we propose a subspace clustering method based on Cauchy loss function (CLF). Particularly, it uses CLF to penalize the noise term for suppressing the large noise mixed in the real data. This is due to that the CLF’s influence function has an upper bound that can alleviate the influence of a single sample, especially the sample with a large noise, on estimating the residuals. Furthermore, we theoretically prove the grouping effect of our proposed method, which means that highly correlated data can be grouped together. Finally, experimental results on five real data sets reveal that our proposed method outperforms several representative clustering methods. [ABSTRACT FROM AUTHOR]

Analytical noise formulations and design optimization of single- and dual-band inductively source-degenerated MOS low-noise amplifiers (LNAs) with the substrate and metal loss effects of on-chip inductors are established in this paper. It reveals that the noise figures of both single-/dual-band LNAs degrade substantially under the consideration of the loss effects. However, by increasing the device size, the noise optimization methodology of simultaneous noise and input match for a single-band LNA still holds true with concern for the loss effects. For a dual-band LNA, analytical noise optimization for a balanced noise design is established for inductors with metal and substrate loss and indicates a larger device size. The substrate and metal loss effects of inductors can be mitigated using integrated passive device (IPD) process for the input match network. The demonstrated single-band 0.18- $\mu \text{m}$ MOS LNAs with and without IPD process show noise figures of 1.53 and 2.52 dB at 2.4 GHz, respectively. Subsequently, the implemented dual-band 0.18- $\mu \text{m}$ MOS LNAs with and without IPD process show noise figures of 1.6/2.6 and 3.25/4.1 dB at 2.4/5 GHz, respectively. [ABSTRACT FROM AUTHOR]