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In complex dynamical networks, pinning control techniques are often applied to control a small fraction of the nodes in order to stabilize the network with reduced control effort and energy, facilitating adequate development of the complex network. Selecting the controlled nodes is a key challenge to achieving optimal performance. Theoretical analysis of the network provides the minimum quantity of nodes to control but does not specify which ones should be controlled. Analytically, controllability analysis of the entire network would be ideal, but this becomes difficult for complex networks with a large number of nodes and non-linear dynamics. Another option is to evaluate all possible combinations with the minimum number of necessary nodes or the nodes that can be controlled, but this presents a computational challenge due to the large number of possible combinations. Therefore, the remaining option is the use of metaheuristic algorithms for the rapid and practical evaluation of these combinations. In this work, we propose to optimize the selection of nodes for pinning control based on binary optimization algorithms, subject to control and development constraints. The proposed approach involves finding a binary combination with a fixed number of controlled nodes that best stabilizes the network state to zero. This paper includes a comparative study among state-of-the-art binary optimization algorithms and modified classic optimization algorithms. The applicability of the proposed approach is validated through simulations considering a dynamical discrete-time complex network. [ABSTRACT FROM AUTHOR]

The quantum-inspired genetic algorithm (QGA), which combines quantum mechanics concepts and GA to enhance search capability, has been popular and provides an efficient search mechanism. This paper proposes a modified QGA, called dynamic QGA (DQGA). The proposed algorithm utilizes a lengthening chromosome strategy for a balanced and smooth transition between exploration and exploitation phases to avoid local optima and premature convergence. Apart from that, a novel adaptive look-up table for rotation gates is presented to boost the algorithm's optimization abilities. To evaluate the effectiveness of these ideas, DQGA is tested by various mathematical benchmark functions as well as real-world constrained engineering problems against several well-known and state-of-the-art algorithms. The obtained results indicate the merits of the proposed algorithm and its superiority for solving multimodal benchmark functions and real-world constrained engineering problems. [ABSTRACT FROM AUTHOR]

The execution of quantum algorithms requires two key considerations. On the one hand, it should meet the connectivity constraint requirements of quantum circuit mapping for quantum architectures, and on the other hand, it needs to consider reducing the probability of errors in the execution of quantum circuits as much as possible. This paper proposes a novel optimization technique based on template matching that to satisfy both requirements. The template matching optimization method can significantly reduce the number of gates in a quantum circuit and further enhance its practicality. It stands as advanced optimization technology available today. Our method optimizes quantum logic circuits mapped onto quantum architecture by initially selecting their linear substructure. We then zone the circuit according to the gate dependency graph and optimize each block through template matching. Finally, we reorganize the circuit to obtain the optimized version as the final result. Our proposed method is amenable to various quantum architectures. To evaluate its efficacy, we conduct a comparative analysis with the t|ket〉 and Qiskit compiler using a set of benchmark test circuits. Specifically, compare to the t|ket〉 compiler method, the highest average optimization rate of our method can reach 25.75%. Compare with the Qiskit compiler method, the highest average optimization rate can reach 32.72%. Overall, our approach has significant optimization advantages. [ABSTRACT FROM AUTHOR]

We consider a function estimation method with change point detection using truncated power spline basis and elastic-net-type L 1 -norm penalty. The L 1 -norm penalty controls the jump detection and smoothness depending on the value of the parameter. In terms of the proposed estimators, we introduce two computational algorithms for the Lagrangian dual problem (coordinate descent algorithm) and constrained convex optimization problem (an algorithm based on quadratic programming). Subsequently, we investigate the relationship between the two algorithms and compare them. Using both simulation and real data analysis, numerical studies are conducted to validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]