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Solving constrained optimization problems that require processing large-scale data is of significant value in practical applications, and such problems can be described as the minimization of a finite-sum of local convex functions. Many existing works addressing constrained optimization problems have achieved a linear convergence rate to the exact optimal solution if the constant step-size was sufficiently small. However, they still suffer from low computational efficiency because of the computation of the local batch gradients at each iteration. Considering high computational efficiency to resolve the constrained optimization problems, we introduce the projection operator and variance-reduction technique to propose a novel projected decentralized variance-reduction algorithm, namely P-DVR, to tackle the constrained optimization problem subject to a closed convex set. Theoretical analysis shows that if the local function is strongly convex and smooth, the P-DVR algorithm can converge to the exact optimal solution at a linear convergence rate O ( λ ^ k) with a sufficiently small step-size, where 0 < λ ^ < 1 . Finally, we experimentally validate the effectiveness of the algorithm, i.e., the algorithm possesses high computational efficiency and exact convergence. [ABSTRACT FROM AUTHOR]

In this article, we consider a class of decentralized resource allocation problems over directed networks, where each node only communicates with its in-neighbors and attempts to minimize its own cost when network-wide resource constraints as well as local capacity limits are satisfied. Decentralized optimization to solve this problem has been a significant focus within engineering research due to its advantages in scalability, robustness, and flexibility. Most existing methods are synchronous while few works are devoted to asynchronously solving the problem. The problem becomes even more challenging when the networks are directed. To address the resource allocation problem when the above issues are considered, we propose a novel decentralized asynchronous algorithm based on the gossip-based communication protocol and epigraph strategy. An important feature of the algorithm is that it is implemented in a completely decentralized manner in the case of asynchronous communication and directed networks. We provide theoretical proof to guarantee the convergence of the proposed algorithm, which indicates that it can successfully allocate the optimal resource. When solving the resource allocation problem over time-varying directed networks, we further discuss a related decentralized asynchronous algorithm according to the random sleep protocol. Numerical examples are given to demonstrate the viability and performance of the algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the global asymptotic and exponential stability of the equilibrium point of discrete-time delayed Hopfield neural networks (DHNN) with large impulse effects. The present results characterize the aggregated effects of impulse and stability property of the impulse-free Hopfield neural networks on the stability of the considered system. The feasible upper bound of impulses is also estimated. It is shown that the discrete-time impulsive DHNN might preserve global exponential stability of the impulse-free DHNN even if the impulses have enlarging effects on the states of neurons. [ABSTRACT FROM PUBLISHER]

n algorithm for constructing a one-way novel Hash function based on two-layer chaotic neural network structure is proposed. The piecewise linear chaotic map (PWLCM) is utilized as transfer function, and the 4-dimensional and one-way coupled map lattices (4D OWCML) is employed as key generator of the chaotic neural network. Theoretical analysis and computer simulation indicate that the proposed algorithm presents several interesting features, such as high message and key sensitivity, good statistical properties, collision resistance and secure against meet-in-the-middle attacks, which can satisfy the performance requirements of Hash function. [ABSTRACT FROM AUTHOR]

In this paper, we reconsider and analyze our previous paper a novel hash algorithm construction based on chaotic neural network, then present equal-length and unequal-length forgery attacks against its security in detail, and then propose a significantly improved approach by utilizing a method of complicated nonlinear computation to enhance the security of the original hash algorithm. Theoretical analysis and computer simulation indicate that the improved algorithm can completely resist the two kinds of forgery attacks and also shows other better performance than the original one, such as better message and key sensitivity, statistical properties, which can satisfy the performance requirements of a more secure hash function. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm for one-way hash function construction based on the chaotic look-up table with changeable parameter. First, the original message with padding is divided into block messages as groups and then further broken into sub-block messages, which are translated into corresponding ASCII codes by groups. Then the group ASCII codes are successively inputted into the chaotic look-up table updated by changeable parameter to permute corresponding values. Finally, based on the calculation of the values, 128-bit hash value is generated. Performance analysis indicates that our proposed algorithm satisfies sensitive requirements and can resist all kinds of attacks. [ABSTRACT FROM AUTHOR]

A parallel Hash algorithm construction based on chaotic maps with changeable parameters is proposed and analyzed in this paper. The two main characteristics of the proposed algorithm are parallel processing mode and message expansion. The algorithm translates the expanded message blocks into the corresponding ASCII code values as the iteration times, iterates the chaotic asymmetric tent map and then the chaotic piecewise linear map, continuously, with changeable parameters dynamically obtained from the position index of the corresponding message blocks, to generate decimal fractions, then rounds the decimal fractions to integers, and finally cascades these integers to construct intermediate Hash value. Final Hash value with the length of 128-bit is generated by logical XOR operation of intermediate Hash values. Theoretical analysis and computer simulation indicate that the proposed algorithm satisfies the performance requirements of a secure Hash function. [ABSTRACT FROM AUTHOR]

Abstract: Recently, a variety of chaos-based hash functions have been proposed. Nevertheless, none of them works efficiently in parallel computing environment. In this Letter, an algorithm for parallel keyed hash function construction is proposed, whose structure can ensure the uniform sensitivity of hash value to the message. By means of the mechanism of both changeable-parameter and self-synchronization, the keystream establishes a close relation with the algorithm key, the content and the order of each message block. The entire message is modulated into the chaotic iteration orbit, and the coarse-graining trajectory is extracted as the hash value. Theoretical analysis and computer simulation indicate that the proposed algorithm can satisfy the performance requirements of hash function. It is simple, efficient, practicable, and reliable. These properties make it a good choice for hash on parallel computing platform. [Copyright &y& Elsevier]

In this paper, we present an optimal packet size solution that optimizes the communication energy consumption in the heterogeneous wireless networks. More specifically, we consider a heterogeneous network system composed of a body sensor network (BSN) and a WiFi network. Then, based on the analysis of data communication in the BSN and WiFi (BSN-WiFi) network, we formulate a communication energy consumption optimization model with the constraints of throughput and time delay. Mathematically, we convert this model into a geometric programming problem, which is then numerically solved. The optimal solution can be applied in both BSN and WiFi network to dynamically select packet payload sizes according to real-time packet delivery ratios (PDRs). Since PDRs are time-varying, we tabulate a packet payload size lookup table for online packet size selection using PDRs as indices. Finally, we collect PDRs from a deployed BSN-WiFi network and evaluate the energy optimization model. The performance evaluation results show that, in comparison with fixed packet size solutions, our optimal solutions achieve up to 70 percent energy savings in a BSN(TDMA)-WiFi network and 68 percent in a BSN(CSMA)-WiFi network. [ABSTRACT FROM AUTHOR]

A new parallel chaotic Hash function, based on four-dimensional cellular neural network, is proposed in this paper. The message is expanded by iterating chaotic logistic map and then divided into blocks with a length of 512 bits each. All blocks are processed in a parallel mode, which is one of the significant characteristics of the proposed algorithm. Each 512-bit block is divided into four 128-bit sub-blocks, each of which is further separated into four 32-bit values and then the four values are mixed into four new values generated by chaotic cat map. The obtained four new values are performed by the bit-wise exclusive OR operation with four initial values or previously generated four values, and then, they are used as the inputs of cellular neural network. By iterating cellular neural network, another four values as the middle Hash value are generated. The generated values of all blocks are inputted into the compression function to produce the final 128-bit Hash value. Theoretical analysis and computer simulation indicate that the proposed algorithm satisfies the requirements of a secure Hash function. [ABSTRACT FROM AUTHOR]