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The global minimum point of an optimization problem is of interest in engineering fields and it is difficult to be found, especially for a nonconvex large-scale optimization problem. In this article, we consider a new memetic algorithm for this problem. That is to say, we use the continuation Newton method with the deflation technique to find multiple stationary points of the objective function and use those found stationary points as the initial seeds of the evolutionary algorithm, other than the random initial seeds of the known evolutionary algorithms. Meanwhile, in order to retain the usability of the derivative-free method and the fast convergence of the gradient-based method, we use the automatic differentiation technique to compute the gradient and replace the Hessian matrix with its finite difference approximation. According to our numerical experiments, this new algorithm works well for unconstrained optimization problems and finds their global minima efficiently, in comparison to the other representative global optimization methods such as the multi-start methods (the built-in subroutine GlobalSearch.m of MATLAB R2021b, GLODS and VRBBO), the branch-and-bound method (Couenne, a state-of-the-art open-source solver for mixed integer nonlinear programming problems), and the derivative-free algorithms (CMA-ES and MCS)., Comment: arXiv admin note: substantial text overlap with arXiv:2103.05829

This paper considers the regularization continuation method and the trust-region updating strategy for the optimization problem with linear equality constraints.The proposed method utilizes the linear conservation law of the regularization continuation method such that it does not need to compute the correction step for preserving the feasibility other than the previous continuation methods and the quasi-Newton updating formulas for the linearly constrained optimization problem. Moreover, the new method uses the special limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) formula as the preconditioning technique to improve its computational efficiency in the well-posed phase, and it uses the inverse of the regularized two-sided projection of the Lagrangian Hessian as the pre-conditioner to improve its robustness. Numerical results also show that the new method is more robust and faster than the traditional optimization method such as the alternating direction method of multipliers (ADMM),the sequential quadratic programming (SQP) method (the built-in subroutine fmincon.m of the MATLAB2020a environment), and the recent continuation method (Ptctr). The computational time of the new method is about 1/3 of that of SQP (fmincon.m). Finally, the global convergence analysis of the new method is also given., Comment: arXiv admin note: substantial text overlap with arXiv:2101.07055, arXiv:2012.14808

We consider the weak target detection problem with unknown parameter in colocated multiple-input multiple-output (MIMO) radar. To cope with the sheer amount of data for large-size systems, a multi-bit quantizer is utilized in the sampling process. As a low-complexity alternative to classic generalized likelihood ratio test (GLRT) for quantized data, we propose the multi-bit detector on Rao test with a closed-form test statistic, whose theoretical asymptotic distribution is provided to generalize the actual detection performance. Besides, we refine the design of quantizer by optimized quantization thresholds, which are obtained resorting to the popular particle swarm optimization algorithmthe (PSOA). The simulation is conducted to demonstrate the performance variations of detectors based on unquantized and quantized data. The numerical results corroborate our theoretical analyses and show that the performance with 3-bit quantization approaches the case without quantization., Comment: 6 pages, 3 figures, conference

This paper considers the generalized continuation Newton method and thetrust-region updating strategy for the underdetermined system of nonlinear equations. Moreover, in order to improve its computational efficiency, the new method will not update the Jacobian matrix when the current Jacobian matrix performs well. The numerical results show that the new method is more robust and faster than the traditional optimization method such as the Levenberg-Marquardt method (a variant of trust-region methods, the built-in subroutine fsolve.m of the MATLAB R2020a environment). The computational time of the new method is about 1/8 to 1/50 of that of fsolve. Furthermore, it also proves the global convergence and the local superlinear convergence of the new method under some standard assumptions., Comment: arXiv admin note: text overlap with arXiv:2006.02634, arXiv:2006.07568

This paper considers an explicit continuation method with the trusty time-stepping scheme and the limited-memory BFGS (L-BFGS) updating formula (Eptctr) for the linearly constrained optimization problem. At every iteration, Eptctr only involves three pairs of the inner product of vector and one matrix-vector product, other than the traditional and representative optimization method such as the sequential quadratic programming (SQP) or the latest continuation method such as Ptctr \cite{LLS2020}, which needs to solve a quadratic programming subproblem (SQP) or a linear system of equations (Ptctr). Thus, Eptctr can save much more computational time than SQP or Ptctr. Numerical results also show that the consumed time of EPtctr is about one tenth of that of Ptctr or one fifteenth to 0.4 percent of that of SQP. Furthermore, Eptctr can save the storage space of an $(n+m) \times (n+m)$ large-scale matrix, in comparison to SQP. The required memory of Eptctr is about one fifth of that of SQP. Finally, we also give the global convergence analysis of the new method under the standard assumptions., Comment: arXiv admin note: substantial text overlap with arXiv:2012.14808; text overlap with arXiv:2005.05965

This paper considers an explicit continuation method and the trust-region updating strategy for the unconstrained optimization problem. Moreover, in order to improve its computational efficiency and robustness, the new method uses the switching preconditioning technique. In the well-conditioned phase, the new method uses the L-BFGS method as the preconditioning technique in order to improve its computational efficiency. Otherwise, the new method uses the inverse of the Hessian matrix as the pre-conditioner in order to improve its robustness. Numerical results aslo show that the new method is more robust and faster than the traditional optimization method such as the trust-region method and the line search method. The computational time of the new method is about one percent of that of the trust-region method (the subroutine fminunc.m of the MATLAB2019a environment, it is set by the trust-region method) or one fifth of that the line search method (fminunc.m is set by the quasi-Newton method) for the large-scale problem. Finally, the global convergence analysis of the new method is also given.

It is generally supposed that a transition from the normal decay phase (decay slope $\sim -1$) to a steeper phase (decay slope $\sim -2$) could be suggested as a jet break. The jet opening angle $\theta_{\rm jet}$ is then calculated from the jet break time of the afterglow light curve. This allows the derivation of the collimation-corrected energy $E_{\rm jet}$ of those GRBs. We extensively searched for the GRBs with jet break features from multi-wavelength afterglow light curves, and 138 GRBs with significant breaks were collected. The jet break times of those GRBs mainly range from 1000 s to $10^6$ s, and the distribution of the collimation-corrected energy $E_{\rm jet}$ peaks at $\sim10^{50}$ erg. We also confirmed the $E_{\rm \gamma,iso}-E_{\rm p,i}$, $E_{\rm jet}-E_{\rm p,i}$ and $E_{\rm \gamma,iso}-\theta_{\rm jet}$ relations, and found $E_{\rm \gamma,iso}-T_{\rm j,z}-E_{\rm p,i}$ relation remains tight with more multi-wavelength data. This tight $E_{\rm \gamma,iso}-T_{\rm j,z}-E_{\rm p,i}$ relation is also conformed by different groups of our selected GRBs in the paper. In addition, another two new and tighter correlations among $E_{\rm jet}-T_{\rm j,z}-E_{\rm p,i}$ are well confirmed for different circumburst mediums in this paper. We suggest that those tight three-parameter correlations are more physical, and could be widely applied to constrain the cosmological parameters., Comment: 11 pages, 4 figures and 2 tables; final version published in ApJ

For nonlinear equations, the homotopy methods (continuation methods) are popular in engineering fields since their convergence regions are large and they are quite reliable to find a solution. The disadvantage of the classical homotopy methods is that their computational time is heavy since they need to solve many auxiliary nonlinear systems during the intermediate continuation processes. In order to overcome this shortcoming, we consider the special explicit continuation Newton method with the residual trust-region time-stepping scheme for this problem. According to our numerical experiments, the new method is more robust and faster to find the required solution of the real-world problem than the traditional optimization method (the built-in subroutine fsolve.m of the MATLAB environment) and the homotopy continuation methods(HOMPACK90 and NAClab). Furthermore, we analyze the global convergence and the local superlinear convergence of the new method.

Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services., Comment: arXiv admin note: text overlap with arXiv:0906.3925 by other authors

Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multioriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost., Comment: Accepted by ICASSP 2020