Search

The complexity of historical data in financial markets and the uncertainty of the future, as well as the idea that investors always expect the least risk and the greatest return. This study presents a multi-period fractional portfolio model in a fuzzy environment, taking into account the limitations of asset quantity, asset position, transaction cost, and inter-period investment. This is a mixed integer programming NP-hard problem. To overcome the problem, an improved genetic algorithm (IGA) is presented. The IGA contribution mostly involves the following three points: (i) A cardinal constraint processing approach is presented for the cardinal constraint conditions in the model; (ii) Logistic chaotic mapping was implemented to boost the initial population diversity; (iii) An adaptive golden section variation probability formula is developed to strike the right balance between exploration and development. To test the model's logic and the performance of the proposed algorithm, this study picks stock data from the Shanghai Stock Exchange 50 for simulated investing and examines portfolio strategies under various limitations. In addition, the numerical results of simulated investment are compared and analyzed, and the results show that the established models are in line with the actual market situation and the designed algorithm is effective, and the probability of obtaining the optimal value is more than 37.5% higher than other optimization algorithms. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, named Velocity Four Sine Cosine Particle Swarm Optimization (VFSCPSO). The introduction of the SCA strategy in the velocity formulation ensures that the global optimal solution is found accurately. It increases the flexibility of PSO. A series of experiments are conducted on the CEC2005 test suite with compositional algorithms, algorithmic variants, and good intelligent algorithms. The experimental results show that the algorithm effectively improves the overall performance of compositional algorithms; the Friedman test proves that the algorithm has good competitiveness. The algorithm also performs better in PID parameter tuning. Therefore, the VFSCPSO is able to solve the high-dimensional global optimization problems in a better way. [ABSTRACT FROM AUTHOR]

This paper constructs an interval partition linearization algorithm for solving minimax linear fractional programming (MLFP) problem. In this algorithm, MLFP is converted and decomposed into a series of linear programs by dividing the outer 1-dimensional space of the equivalent problem (EP) into polynomially countable intervals. To improve the computational efficiency of the algorithm, two new acceleration techniques are introduced, in which the regions in outer space where the optimal solution of EP does not exist are largely deleted. In addition, the global convergence of the proposed algorithm is summarized, and its computational complexity is illustrated to reveal that it is a fully polynomial time approximation scheme. Finally, the numerical results demonstrate that the proposed algorithm is feasible and effective. [ABSTRACT FROM AUTHOR]

This paper investigates a class of linear fractional programming (LFP) problem, which minimizes the sum of a finite number of linear fractional functions over a polyhedral region. Firstly, the equivalence problem (EP) of the LFP problem is given by a new two-stage transformation method. Secondly, considering the characteristics that the branch-and-bound algorithm can guarantee the global optimality of the solution to an optimization problem, and then based on the EP, we discuss the bounding operation, branching operation, pruning operation and rectangle-region reduction technique of this algorithm. After that, the convergence of the algorithm is proved and its computational complexity is deduced from the worst case. Finally, some experiments are reported to verify the effectiveness, feasibility and other performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To apply biogeography-based optimization (BBO) to large scale optimization problems, this paper proposes a novel BBO variant based on feedback differential evolution mechanism and steepest descent method, referred to as FBBOSD. Firstly, the immigration refusal mechanism is proposed to eliminate the damage of inferior solutions to superior solutions. Secondly, the dynamic hybrid migration operator is designed to balance the exploration and exploitation, which makes BBO suitable for high-dimensional environment. Thirdly, the feedback differential evolution mechanism is designed to make FBBOSD can select mutation modes intelligently. Finally, the steepest descent method is creatively combined with BBO, which further improves the convergence accuracy. Meanwhile, a sequence convergence model is established to prove the convergence of FBBOSD. Quantitative evaluations: FBBOSD is compared with BBO, seven BBO variants and seven state-of-the-art evolutionary algorithms, respectively. The experimental results on 24 benchmark functions and CEC2017 show that FBBOSD outperforms all compared algorithms, and the dimension of solving optimization problems can reach 10,000. Then, FBBPOSD is applied to engineering design problems. The simulation results demonstrate that it is also effective on constrained optimization problems. In short, FBBOSD has excellent performance and outstanding stability, which is a new algorithm worthy of adoption and promotion. [ABSTRACT FROM AUTHOR]

The "Curse of Dimensionality" induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. Feature selection (FS) can eliminate irrelevant information in big data and improve accuracy. As a recently proposed algorithm, the Sparrow Search Algorithm (SSA) shows its advantages in the FS tasks because of its superior performance. However, SSA is more subject to the population's poor diversity and falls into a local optimum. Regarding this issue, we propose a variant of the SSA called the Tent Lévy Flying Sparrow Search Algorithm (TFSSA) to select the best subset of features in the wrapper-based method for classification purposes. After the performance results are evaluated on the CEC2020 test suite, TFSSA is used to select the best feature combination to maximize classification accuracy and simultaneously minimize the number of selected features. To evaluate the proposed TFSSA, we have conducted experiments on twenty-one datasets from the UCI repository to compare with nine algorithms in the literature. Nine metrics are used to evaluate and compare these algorithms' performance properly. Furthermore, the method is also used on the coronavirus disease (COVID-19) dataset, and its classification accuracy and the average number of feature selections are 93.47% and 2.1, respectively, reaching the best. The experimental results and comparison in all datasets demonstrate the effectiveness of our new algorithm, TFSSA, compared with other wrapper-based algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of maximizing the sum of a concave–convex quadratic fractional function on a non-empty, bounded, convex quadratic feasible set, which is called the problem (QCQFP). By using a conventional semidefinite program (SDP) relaxation, QCQFP is reformulated as a class of linear fractional programming problems over the cone of positive semidefinite matrices, which we refer to these problems as SDFP. After expatiating the properties of SDFP, we transform SDFP into its equivalent problem (ESDFP) whose non-convexity is mainly reflected in the newly added nonlinear equality constraints. By relaxing these nonlinear equality constraints into linear constraints, a special SDP relaxation is generated for ESDFP. Based on these results, an effective branch-and-bound algorithm is designed, and its theoretical convergence and worst-case complexity are proved. Numerical experiments demonstrate that the proposed algorithm is effective and feasible. [ABSTRACT FROM AUTHOR]

Feature selection is an essential step in the preprocessing of data in pattern recognition and data mining. Nowadays, the feature selection problem as an optimization problem can be solved with nature-inspired algorithm. In this paper, we propose an efficient feature selection method based on the cuckoo search algorithm called CBCSEM. The proposed method avoids the premature convergence of traditional methods and the tendency to fall into local optima, and this efficient method is attributed to three aspects. Firstly, the chaotic map increases the diversity of the initialization of the algorithm and lays the foundation for its convergence. Then, the proposed two-population elite preservation strategy can find the attractive one of each generation and preserve it. Finally, Lévy flight is developed to update the position of a cuckoo, and the proposed uniform mutation strategy avoids the trouble that the search space is too large for the convergence of the algorithm due to Lévy flight and improves the algorithm exploitation ability. The experimental results on several real UCI datasets show that the proposed method is competitive in comparison with other feature selection algorithms. [ABSTRACT FROM AUTHOR]

This paper explains a region-division-linearization algorithm for solving a class of generalized linear multiplicative programs (GLMPs) with positive exponent. In this algorithm, the original nonconvex problem GLMP is transformed into a series of linear programming problems by dividing the outer space of the problem GLMP into finite polynomial rectangles. A new two-stage acceleration technique is put in place to improve the computational efficiency of the algorithm, which removes part of the region of the optimal solution without problems GLMP in outer space. In addition, the global convergence of the algorithm is discussed, and the computational complexity of the algorithm is investigated. It demonstrates that the algorithm is a complete polynomial time approximation scheme. Finally, the numerical results show that the algorithm is effective and feasible. [ABSTRACT FROM AUTHOR]

For the minimization of the sum of linear fractions on polyhedra, it is likewise a class of linear fractional programming (LFP). In this paper, we mainly propose a new linear relaxation technique and combine the branch-and-bound algorithm framework to solve the LFP globally. It is worthwhile to mention that the branching operation of the algorithm occurs in the relatively small output space of the dimension rather than the space where the decision variable is located. When the number of linear fractions in the objective function is much lower than the dimension of the decision variable, the performance of the algorithm is better. After that, we also explain the effectiveness, feasibility, and other performances of the algorithm through numerical experiments. [ABSTRACT FROM AUTHOR]

In the financial market, investors must deal with uncertain risk, and they also face background risk and many uncertain factors caused by their own characteristics. Considering the fuzzy nature of these factors as well as investors' risk preferences, transaction costs, and so on, in order to reduce investment risk, an improved probability entropy measure is introduced, and a probability mean-lower semivariance-entropy model with different risk attitudes is established by using fuzzy sets and probability theory. To solve the portfolio model, an improved differential evolution algorithm is proposed and a numerical example is given. The numerical results show that the proposed algorithm is effective and that the model can disperse the financial risk to a certain extent and reasonably solve the portfolio problem under many different conditions. [ABSTRACT FROM AUTHOR]

The biogeography-based optimization (BBO) algorithm is known for its simplicity and low computational overhead, but it often struggles with falling into local optima and slow convergence speed. Against this background, this work presents a multi-strategy enhanced BBO variant, named MSBBO. Firstly, the example chasing strategy is proposed to eliminate the destruction of the inferior solutions to superior solutions. Secondly, the heuristic crossover strategy is designed to enhance the search ability of the population. Finally, the prey search–attack strategy is used to balance the exploration and exploitation. To verify the performance of MSBBO, we compare it with standard BBO, seven BBO variants (PRBBO, BBOSB, HGBBO, FABBO, BLEHO, MPBBO and BBOIMAM) and seven meta-heuristic algorithms (GWO, WOA, SSA, ChOA, MPA, GJO and BWO) on multiple dimensions of 24 benchmark functions. It concludes that MSBBO significantly outperforms all competitors both on convergence accuracy, speed and stability, and MSBBO basically converges to the same results on 10,000 dimensions as on 1000 dimensions. Further, MSBBO is applied to six real-world engineering design problems. The experimental results show that our work is still more competitive than other latest optimization techniques (COA, EDO, OMA, SHO and SCSO) on constrained optimization problems. [ABSTRACT FROM AUTHOR]

A new definition of the filled function is presented in this paper. Based on the definition, a new filled function is presented, and a global optimization algorithm is developed. The implementation of the algorithm on several test problems is reported with satisfactory numerical results. The numerical experiments further show that the algorithm may solve higher dimensional global optimization problems. [ABSTRACT FROM AUTHOR]

For the nonconvex quadratic programming problem, a new linear programming relaxation bound-and-reduce algorithm is proposed and its convergence is proved. In this algorithm, a new hyper-rectangle partition technique and a new linear programming relaxation tactics are used. At the same time, the hyper-rectangular reduction method is used to raise its convergent speed. The numerical results demonstrate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate and develop a new filled function method for solving integer programming problems with constraints. By adopting the appropriate equivalent transformation method, these problems are transformed into a class of box-constrained integer programming problems. Then, an effective nonparametric filled function is constructed, and a new global optimization algorithm is designed using the discrete steepest descent method. Numerical experiments illustrate that this algorithm has effectiveness, feasibility, and better global optimization ability. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization(NMF) is a powerful image representation algorithm in pattern recognition and data mining. However, the traditional NMF does not utilize any label information, or fail to guarantee the sparse parts-based representation. In this paper, we put forward a semi-supervised local coordinate NMF (SLNMF) algorithm, which incorporate the available label information and the local coordinate constraint into NMF. Particularly, SLNMF makes the learned coefficients sparse by adding the coordinate constraint, and enhance the discriminative ability of different classes by using the label information constraint. Furthermore, in order to extract the geometric structure of the data space, we propose a new semi-supervised graph regularized NMF with local coordinate constraint (SGLNMF) method, which incorporates the graph regularization into SLNMF to enhance the discriminative abilities of data representations. SGLNMF not only reveals the intrinsic geometrical information of the data space, but also takes into account the local coordinate constraint and the label information. Clustering experiments on several standard image datasets demonstrate the effectiveness of our proposed SLNMF and SGLNMF methods compared to the state-of-the-art methods. • In our proposed SLNMF, which incorporates the label information and the local coordinate constraint into NMF as the additional constraints to enhance the performance of the clustering algorithm. • SGLNMF reveals the intrinsic geometrical information of the data space by constructing the graph regularization, and also takes both the local coordinate constraints and the label information into account, so it can have better performance on the AC and NMI. • We develop the corresponding iterative updating optimization schemes to derive the iterative updating rules of matric factors U and Z. More importantly, the convergence proof of our proposed SGLNMF is provided. [ABSTRACT FROM AUTHOR]

In this paper, a new filled function method for finding a global minimizer of global optimization is proposed. The proposed filled function is continuously differentiable and only contains one parameter. It has no parameter sensitive terms. As a result, a general classical local optimization method can be used to find a better minimizer of the proposed filled function with easy parameter adjustment. Numerical experiments show that the proposed filled function method is effective. [ABSTRACT FROM AUTHOR]

Quadratically constrained quadratic programs (QCQP), which often appear in engineering practice and management science, and other fields, are investigated in this paper. By introducing appropriate auxiliary variables, QCQP can be transformed into its equivalent problem (EP) with non-linear equality constraints. After these equality constraints are relaxed, a series of linear relaxation subproblems with auxiliary variables and bound constraints are generated, which can determine the effective lower bound of the global optimal value of QCQP. To enhance the compactness of sub-rectangles and improve the ability to remove sub-rectangles, two rectangle-reduction strategies are employed. Besides, two ϵ -subproblem deletion rules are introduced to improve the convergence speed of the algorithm. Therefore, a relaxation and bound algorithm based on auxiliary variables are proposed to solve QCQP. Numerical experiments show that this algorithm is effective and feasible. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective differential evolution algorithm with multiple trial vectors. For each individual in the population, three trial individuals are produced by the mutation operator. The offspring is produced by using the crossover operator on the three trial individuals. Good individuals are selected from the parent and the offspring and then are put in the intermediate population. Finally, the intermediate population is sorted according to the Pareto dominance relations and the crowding distance, and then the outstanding individuals are selected as the next evolutionary population. Comparing with the classical multiobjective optimization algorithm NSGA-II, the proposed algorithm has better convergence, and the obtained Pareto optimal solutions have better diversity [ABSTRACT FROM AUTHOR]

Abstract: In this paper, by solving the relaxed quasiconcave programming problem in outcome space, a new global optimization algorithm for convex multiplicative programming is presented. Two kinds of techniques are employed to establish the algorithm. The first one is outer approximation technique which is applied to shrink relaxation area of quasiconcave programming problem and to compute appropriate feasible points and to raise the capacity of bounding. And the other one is branch and bound technique which is used to guarantee global optimization. Some numerical results are presented to demonstrate the effectiveness and feasibility of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents an outcome-space finite algorithm for solving linear multiplicative programming, in each iteration of which a convex quadratic programming is only solved. In the paper, we give a global optimization condition on a class of multiplicative programming problems and prove that the proposed algorithm is finite terminative and gain a global optimal solution of the former problem when it stops. It can be shown by the numerical results that the proposed algorithm is effective and the computational results can be gained in short time. [Copyright &y& Elsevier]

Abstract: The paper presents a new rectangle branch-and-reduce approach for solving nonconvex quadratic programming problems, in which a new lower approximate linear functions of the quadratic function over an n-rectangle is given to determine a lower bound of the global optimal value of the original problem over each rectangle, and a simple two-partition technique on rectangle is used, as well as the tactics on reducing and deleting subrectangles is used to accelerate the convergence of the proposed algorithm. The proposed algorithm is proved to be convergent and shown to be effective with numerical results. [Copyright &y& Elsevier]

In solving classification problems in the field of machine learning and pattern recognition, the pre-processing of data is particularly important. The processing of high-dimensional feature datasets increases the time and space complexity of computer processing and reduces the accuracy of classification models. Hence, the proposal of a good feature selection method is essential. This paper presents a new algorithm for solving feature selection, retaining the selection and mutation operators from traditional genetic algorithms. On the one hand, the global search capability of the algorithm is ensured by changing the population size, on the other hand, finding the optimal mutation probability for solving the feature selection problem based on different population sizes. During the iteration of the algorithm, the population size does not change, no matter how many transformations are made, and is the same as the initialized population size; this spatial invariance is physically defined as symmetry. The proposed method is compared with other algorithms and validated on different datasets. The experimental results show good performance of the algorithm, in addition to which we apply the algorithm to a practical Android software classification problem and the results also show the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

In this study, we investigate a class of linear multiplicative programs with positive exponents. By introducing p additional variables, the original problem is reformulated into an equivalent problem (EP) within the output space. Subsequently, a novel global optimization algorithm is introduced to tackle EP. The algorithm primarily leverages two key techniques. One is the outer approximation technique, which tightens the relaxed feasible region of EP and improves the upper bound by carefully examining suitable feasible points. The other is the branch and bound technique to guarantee the global optimality of the solution. Numerical results confirm the effectiveness and practicality of the proposed algorithm, thereby underscoring its potential for real-world applications. [ABSTRACT FROM AUTHOR]

Job shop scheduling problem (JSSP) has high theoretical and practical significance in academia and manufacturing respectively. Therefore, scholars in many different fields have been attracted to study this problem, and many meta-heuristic algorithms have been proposed to solve this problem. As a meta-heuristic algorithm, particle swarm optimization (PSO) has been used to optimize many practical problems in industrial manufacturing. This paper proposes a hybrid PSO enhanced with nonlinear inertia weight and and Gaussian mutation (NGPSO) to solve JSSP. Nonlinear inertia weight improves local search capabilities of PSO, while Gaussian mutation strategy improves the global search ability of NGPSO, which is beneficial to the population to maintain diversity and reduce probability of the algorithm falling into the local optimal solution. The proposed NGPSO algorithm is implemented to solve 62 benchmark instances of JSSP, and the experimental results are compared with other algorithms. The results obtained by analyzing the experimental data show that the algorithm is better than other comparison algorithms in solving JSSP. [ABSTRACT FROM AUTHOR]

In this paper, a new relaxation bounding method is proposed for a class of linear multiplicative programs. Although the 2 p − 1 variable is introduced in the construction of equivalence problem, the branch process of the algorithm is only carried out in p − dimensional space. In addition, a super-rectangular reduction technique is also given to greatly improve the convergence rate. Furthermore, we construct an output-space branch-and-bound reduction algorithm based on solving a series of linear programming sub-problems, and prove the convergence and computational complexity of the algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, we carried out a series of numerical experiments and analyzed the advantages and disadvantages of the algorithm by numerical results. [ABSTRACT FROM AUTHOR]

There are some processing problems of imbalanced data such as imbalanced data sets being difficult to integrate efficiently. This paper proposes and constructs a mixed penalty function data integration screening criterion, and proposes Differential Evolution Integration Algorithm Based on Mixed Penalty Function Screening Criteria (DE-MPFSC algorithm). In addition, the theoretical validity and the convergence of the DE-MPFSC algorithm are analyzed and proven by establishing the Markov sequence and Markov evolution process model of the DE-MPFSC algorithm. In this paper, the entanglement degree and enanglement degree error are introduced to analyze the DE-MPFSC algorithm. Finally, the effectiveness and stability of the DE-MPFSC algorithm are verified by UCI machine learning datasets. The test results show that the DE-MPFSC algorithm can effectively improve the effectiveness and application of imbalanced data classification and integration, improve the internal classification of imbalanced data and improve the efficiency of data integration. [ABSTRACT FROM AUTHOR]

The differential evolutionary ( D E ) algorithm is a global optimization algorithm. To explore the convergence implied in the H i l b e r t space with the parameter β of the D E algorithm and the quantum properties of the optimal point in the space, we establish a control convergent iterative form of a higher-order differential equation under the conditions of P − ε and analyze the control convergent properties of its iterative sequence; analyze the three topological structures implied in H i l b e r t space of the single-point topological structure, branch topological structure, and discrete topological structure; and establish and analyze the association between the H e i s e n b e r g uncertainty quantum characteristics depending on quantum physics and its topological structure implied in the β -Hilbert space of the D E algorithm as follows: The speed resolution Δ v 2 of the iterative sequence convergent speed and the position resolution Δ x β ε of the global optimal point with the swinging range are a pair of conjugate variables of the quantum states in β -Hilbert space about eigenvalues λ i ∈ R , corresponding to the uncertainty characteristics on quantum states, and they cannot simultaneously achieve bidirectional efficiency between convergent speed and the best point precision with any procedural improvements. Where λ i ∈ R is a constant in the β -Hilbert space. Finally, the conclusion is verified by the quantum numerical simulation of high-dimensional data. We get the following important quantitative conclusions by numerical simulation: except for several dead points and invalid points, under the condition of spatial dimension, the number of the population, mutated operator, crossover operator, and selected operator are generally decreasing or increasing with a variance deviation rate + 0.50 and the error of less than ± 0.5 ; correspondingly, speed changing rate of the individual iterative points and position changing rate of global optimal point β exhibit a inverse correlation in β -Hilbert space in the statistical perspectives, which illustrates the association between the H e i s e n b e r g uncertainty quantum characteristics and its topological structure implied in the β -Hilbert space of the D E algorithm. [ABSTRACT FROM AUTHOR]

Obtaining high convergence and uniform distributions remains a major challenge in most metaheuristic multi-objective optimization problems. In this article, a novel multi-objective particle swarm optimization (PSO) algorithm is proposed based on Gaussian mutation and an improved learning strategy. The approach adopts a Gaussian mutation strategy to improve the uniformity of external archives and current populations. To improve the global optimal solution, different learning strategies are proposed for non-dominated and dominated solutions. An indicator is presented to measure the distribution width of the non-dominated solution set, which is produced by various algorithms. Experiments were performed using eight benchmark test functions. The results illustrate that the multi-objective improved PSO algorithm (MOIPSO) yields better convergence and distributions than the other two algorithms, and the distance width indicator is reasonable and effective. [ABSTRACT FROM AUTHOR]

It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is proposed in this paper. First, points in a non-dominated solution set are mapped to a parallel-cell coordinate system. Then, the status of the particles is evaluated by the Pareto entropy and difference entropy. At the same time, the algorithm parameters are adjusted by feedback information. At the late stage of the algorithm, the local-search ability of the particle swarm still needs to be improved. Logistic mapping and the neighboring immune operator are used to maintain and change the external archive. Experimental test results show that the convergence and diversity of the algorithm are improved. [ABSTRACT FROM AUTHOR]

Let be a compact metric space and be an -tuple of continuous selfmaps on . This paper investigates Hausdorff metric Li-Yorke chaos, distributional chaos and distributional chaos in a sequence from a set-valued view. On the basis of this research, we draw the main conclusions as follows: (i) If has a distributionally chaotic pair, especially is distributionally chaotic, the strongly nonwandering set contains at least two points. (ii) We give a sufficient condition for to be distributionally chaotic in a sequence and chaotic in the strong sense of Li-Yorke. Finally, an example to verify (ii) is given. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a class of augmented Lagrangians is considered, for solving equality constrained nonlinear optimization problems via unconstrained minimization techniques. This class of augmented Lagrangians is obtained by multiplying the penalty term on the first order necessary optimality condition in a class of augmented Lagrangians of Di Pillo and Grippo by a penalty parameter. Under suitable assumptions, the exactly corresponding relationship is established between the solution of the original constrained problem and the unconstrained minimization of this class of augmented Lagrangians on the product space of problem variables and multipliers for sufficiently large but finite values of penalty parameters. Therefore, a solution of the original constrained problem and the corresponding values of the Lagrange multipliers can be found by performing a single unconstrained minimization of an augmented Lagrangian on the product space of problem variables and multipliers. In particular, for quadratic programming problems with equality constraints, the optimizer is obtained by minimizing a quadratic function on the expanded space. [Copyright &y& Elsevier]

Abstract: In this paper a deterministic global optimization algorithm is proposed for locatingthe global minimum of the generalized geometric programming (GGP) problem. By utilizing an exponential variable transformation and some other techniques the initial nonconvex problem (GGP) is reduced to a typical reverse convex programming (RCP). Then a linear relaxation of problem (RCP) is obtained based on the famous arithmetic-geometric mean inequality and the linear upper bound of the reverse constraints inside some hyperrectangle region. The proposed branch and bound algorithm is convergent to the global minimum through the successive refinement of the linear relaxation of the feasible region of the objective function and the solutions of a series of linear optimization problems. And finally the numerical experiment is given to illustrate the feasibility and the robust stability of the present algorithm. [Copyright &y& Elsevier]

Myocardial infarction (MI)-induced the activation of NLRP3 inflammasome has been well known to aggravate myocardial injury and cardiac dysfunction by causing inflammation and pyroptosis in the heart. Circular RNAs (circRNAs) have been demonstrated to play critical roles in cardiovascular diseases. However, the functions and mechanisms of circRNAs in modulating cardiac inflammatory response and cardiomyocyte pyroptosis remain largely unknown. We revealed that circHelz, a novel circRNA transcribed from the helicase with zinc finger (Helz) gene, was significantly upregulated in both the ischemic myocardium of MI mouse and neonatal mouse ventricular cardiomyocytes (NMVCs) exposed to hypoxia. Overexpression of circHelz caused cardiomyocyte injury in NMVCs by activating the NLRP3 inflammasome and inducing pyroptosis, while circHelz silencing reduced these effects induced by hypoxia. Furthermore, knockdown of circHelz remarkably attenuated NLRP3 expression, decreased myocardial infarct size, pyroptosis, inflammation, and increased cardiac function in vivo after MI. Overexpression of miR-133a-3p in cardiomyocytes greatly prevented pyroptosis in the presence of hypoxia or circHelz by targeting NLRP3 in NMVCs. Mechanistically, circHelz functioned as an endogenous sponge for miR-133a-3p via suppressing its activity. Overall, our results demonstrate that circHelz causes myocardial injury by triggering the NLRP3 inflammasome-mediated pro-inflammatory response and subsequent pyroptosis in cardiomyocytes by inhibiting miR-133a-3p function. Therefore, interfering with circHelz/miR-133a-3p/NLRP3 axis might be a promising therapeutic approach for ischemic cardiac diseases. Underlying mechanisms of circHelz in the regulation of myocardial infarction. [Display omitted] • Silencing CircHelz improves cardiac function and decreases infarct size post-AMI. • CircHelz leads to cardiomyocyte injury by activating NLRP3 inflammasome induced pyroptosis. • CircHelz reduces miR-133a-3p activity by acting as its endogenous sponge. • CircHelz/miR-133a/NLRP3 is a novel therapeutic target for ischemic heart disease. [ABSTRACT FROM AUTHOR]

Background: Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression, ignore the intrinsic geometric structure in the hyperspectral data. Results: In this paper, we propose a novel model, called manifold regularized collaborative sparse regression, by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Conclusions: Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new discrete filled function method is proposed for finding a global minimizer of integer programming problems. Only one parameter is included in the proposed filled function and it does not need to be adjusted further when it is taken as large as possible; moreover, the current local minimizer obtained by minimizing the proposed filled function will be one of the local minimizers of the original problem and it is better than the minimizers found previously. Thus it is not necessary to use a local search method to the original function. As a result, the computation cost of the proposed discrete filled function method is relatively low. Numerical results demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

• Cardiomyocyte contributes to elevation of Dectin-1 expression post-MI. • Silencing Dectin-1 improves cardiac remodeling and function in the ischemic heart. • Dectin-1 aggravates MI induced cardiac dysfunction by partially activating NF- κ B/NLRP3 axis. The myocardial inflammatory response is a consequence of myocardial infarction (MI), which may deteriorate cardiac remodeling and lead to dysfunction in the heart post-MI. Dectin-1 is a c-type lectin, which has been shown to regulate innate immune responses to pathogens. However, the role of Dectin-1 in the heart diseases remains largely unknown. In this study, we aimed to investigate the effects of Dectin-1 on cardiac remodeling post-MI. We found that cardiac Dectin-1 mRNA and protein expressions were significantly elevated in C57BL/6 mice after MI. In vitro , hypoxia induced cardiomyocyte injury in parallel with increased Dectin-1 protein expression. Knockdown of Dectin-1 remarkably attenuated cardiomyocyte death under hypoxia and lipopolysaccharide (LPS) stimulation. In vivo administration of adeno-associated virus serotype 9 mediated silencing of Dectin-1, which significantly decreased cardiac fibrosis, dilatation, and improved cardiac function in the mice post-MI. At the molecular level, downregulation of Dectin-1 dramatically suppressed up-regulation of nuclear factor-κB (NF-κB), nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3), and the inflammatory genes involved in fibrogenesis and cardiac remodeling after MI. Furthermore, treatment with BAY11-7082, an inhibitor of NF-κB, repressed the activation of NF-κB, and attenuated LPS induced elevation of NLRP3 and cell death in cardiomyocytes. Collectively, upregulation of Dectin-1 in cardiomyocytes post-MI contributes to cardiac remodeling and cardiac dysfunction at least partially by activating NF-κB and NLRP3. This study identified Dectin-1 as a promising therapeutic target for ischemic heart disease. [ABSTRACT FROM AUTHOR]