Quasi-Newton Method for Absolute Value Equation Based on Upper Uniform Smoothing Approximation

In this paper, an upper uniform smooth approximation function of absolute value function is proposed, and some properties of uniform smooth approximation function are studied. Then, absolute value equation (AVE), Ax - |x| = b, where A is a square matrix whose singular values exceed one, is transformed into smooth optimization problem by using the upper uniform smooth approximation function, and solved by quasi-Newton method. Numerical results in solving given AVE problems demonstrated that our algorithm is valid and superior to lower uniform smooth approximation function

Iteration Methods for Linear Systems with Positive Definite Matrix

Two classical first order iteration methods, Richardson iteration and HSS iteration for linear systems with positive definite matrix, are demonstrated. Theoretical analyses and computational results show that the HSS iteration has the advantages of fast convergence speed, high computation efficiency, and without requirement of symmetry

Improved Harmony Search Algorithm with Chaos for Absolute Value Equation

 In this paper, an improved harmony search with chaos (HSCH) is presented for solving NP-hard absolute value equation (AVE) Ax - |x| = b, where A is an arbitrary square matrix whose singular values exceed one. The simulation results in solving some given AVE problems demonstrate that the HSCH algorithm is valid and outperforms the classical HS algorithm (HS) and HS algorithm with differential mutation operator (HSDE)

An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems

Low-Rank Representation for Incomplete Data

Low-rank matrix recovery (LRMR) has been becoming an increasingly popular technique for analyzing data with missing entries, gross corruptions, and outliers. As a significant component of LRMR, the model of low-rank representation (LRR) seeks the lowest-rank representation among all samples and it is robust for recovering subspace structures. This paper attempts to solve the problem of LRR with partially observed entries. Firstly, we construct a nonconvex minimization by taking the low rankness, robustness, and incompletion into consideration. Then we employ the technique of augmented Lagrange multipliers to solve the proposed program. Finally, experimental results on synthetic and real-world datasets validate the feasibility and effectiveness of the proposed method

A standing Leidenfrost drop with Sufi-whirling

The mobility of Leidenfrost drop has been exploited for the manipulation of drop motions. In the classical model, the Leidenfrost drop was levitated by a vapor cushion, in the absence of touch to the surface. Here we report a standing Leidenfrost state on a heated hydrophobic surface where drop stands on the surface with partial adhesion and further self-rotates like Sufi-whirling. To elucidate this new phenomenon, we imaged the evolution of the partial adhesion, the inner circulation, and the ellipsoidal rotation of the drop. The stable partial adhesion is accompanied by thermal and mechanical equilibrium, and further drives the development of the drop rotation.Comment: 16 pages, 4 figure

Biogeography-Based Optimization with Orthogonal Crossover

Biogeography-based optimization (BBO) is a new biogeography inspired, population-based algorithm, which mainly uses migration operator to share information among solutions. Similar to crossover operator in genetic algorithm, migration operator is a probabilistic operator and only generates the vertex of a hyperrectangle defined by the emigration and immigration vectors. Therefore, the exploration ability of BBO may be limited. Orthogonal crossover operator with quantization technique (QOX) is based on orthogonal design and can generate representative solution in solution space. In this paper, a BBO variant is presented through embedding the QOX operator in BBO algorithm. Additionally, a modified migration equation is used to improve the population diversity. Several experiments are conducted on 23 benchmark functions. Experimental results show that the proposed algorithm is capable of locating the optimal or closed-to-optimal solution. Comparisons with other variants of BBO algorithms and state-of-the-art orthogonal-based evolutionary algorithms demonstrate that our proposed algorithm possesses faster global convergence rate, high-precision solution, and stronger robustness. Finally, the analysis result of the performance of QOX indicates that QOX plays a key role in the proposed algorithm

Novel Global Harmony Search Algorithm for General Linear Complementarity Problem

Linear complementarity problem (LCP) is studied. After reforming general LCP as the system of nonlinear equations by NCP-function, LCP is equivalent to solving an unconstrained optimization model, which can be solved by a recently proposed algorithm named novel global harmony search (NGHS). NGHS algorithm can overcome the disadvantage of interior-point methods. Numerical results show that the NGHS algorithm has a higher rate of convergence than the other HS variants. For LCP with a unique solution, NGHS converges to its unique solution. For LCP with multiple solutions, NGHS can find as many solutions as possible. Meanwhile, for unsolvable LCP, all algorithms are terminated on the solution with the minimum error