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Abstract: Shape detection is a fundamental problem in image processing field. In shape detection, lines, circles, and ellipses are the three most important features. In the past four decades, the robustness and the time speedup are two main concerned issues in most developed algorithms. Previously, many randomized algorithms were developed to speed up the computation of the relevant detection successfully. This paper does focus on the time speedup issue. Based on Bresenham’s drawing paradigm, this paper first presents a novel lookup table (LUT)-based voting platform. According to the proposed LUT-based voting platform, we next present a novel computational scheme to significantly speed up the computation of some existing randomized algorithms for detecting lines, circles, and ellipses. Moreover, the detailed time complexity analyses are provided for the three concerned features under our proposed computational scheme and these derived nontrivial analyses also show the relevant computational advantage. Under some real images, experimental results illustrate that our proposed computational scheme can significantly speed up the computation of some existing randomized algorithms. In average, the execution-time improvement ratios are about 28%, 56%, and 48% for detecting lines, circles, and ellipses, respectively, and these improvement ratios are vary close to the theoretic analyses. [Copyright &y& Elsevier]

Abstract: In this paper, by using the theories of mathematical analysis, the authors systematically studied the problem of integrating of trigonometric functions utilizing omnipotence expressions tan . A new integral algorithm for the integral of trigonometric functions was established. And a new Maple program trigint for the calculation mechanizing for the integral of trigonometric functions was established, trigint could evaluate not only the integral of trigonometric functions utilizing omnipotence expresssions correctly but also all the other kinds of functions. The results indicated that the algorithm established in this paper had advantages of simple idea, excellent property for operation and powerful competence. This would be useful for some calculating problems of engineering mathematics. [Copyright &y& Elsevier]

The successive projection algorithm (SPA) has been known to work well for separable nonnegative matrix factorization (NMF) problems arising in applications, such as topic extraction from documents and endmember detection in hyperspectral images. One of the reasons is in that the algorithm is robust to noise. Gillis and Vavasis showed in [8] that a preconditioner can further enhance its noise robustness. The proof rested on the condition that the dimension d and factorization rank r in the separable NMF problem coincide with each other. However, it may be unrealistic to expect that the condition holds in separable NMF problems appearing in actual applications; in such problems, d is usually greater than r . This paper shows, without the condition d = r , that the preconditioned SPA is robust to noise. [ABSTRACT FROM AUTHOR]

Linear Discriminant Analysis (LDA) is a standard tool for classification and dimension reduction in many applications. However, the problem of high dimension is still a great challenge for the classical LDA. In this paper we consider the supervised pattern classification in the high dimensional setting, in which the number of features is much larger than the number of observations and present a novel approach to the sparse optimal scoring problem using the zero-norm. The difficulty in treating the zero-norm is overcome by using appropriate continuous approximations such that the resulting problems are solved by alternating schemes based on DC (Difference of Convex functions) programming and DCA (DC Algorithms). The experimental results on both simulated and real datasets show the efficiency of the proposed algorithms compared to the five state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A BC-tree (block-cutpoint-tree) is a tree (with at least two vertices) where the distance between any two leaves is even. A BC-subtree is a subtree of a connected graph that is also a BC-tree. In this paper, we first consider subtrees containing a specific vertex with conditions on the parity of the distances from this vertex to the leaves and the enumeration of such subtrees of unicyclic and edge-disjoint bicyclic graphs. Using these results and through generating functions of BC-subtrees, we present graph-theoretical based algorithms for enumerating BC-subtrees, BC-subtrees containing a given vertex, and BC-subtrees containing two distinct vertices of unicyclic and edge-disjoint bicyclic graphs. These results also provide a novel perspective on exploring the structural properties of molecules. [ABSTRACT FROM AUTHOR]

An r -dynamic k -coloring of a graph G is a proper vertex k -coloring of G such that the neighbors of any vertex v receive at least min { r , deg ( v ) } different colors. The r -dynamic chromatic number of G , χ r ( G ) , is defined as the smallest k such that G admits an r -dynamic k -coloring. In this paper, first we introduce an upper bound for χ r ( G ) in terms of r , chromatic number, maximum degree and minimum degree. Then, motivated by a conjecture of Montgomery (2001) stating that for a d -regular graph G , χ 2 ( G ) − χ ( G ) ≤ 2 , we prove two upper bounds for χ 2 − χ on regular graphs. Our first upper bound ⌈ 5.437 log d + 2.721 ⌉ improves a result of Alishahi (2011). Also, our second upper bound shows that Montgomery’s conjecture is implied by the existence of a χ ( G ) -coloring for any regular graph G , such that any two vertices whose neighbors are unicolored in this coloring, have no common neighbor. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new filled function, and give an efficient criterion to choose its two parameters appropriately. An algorithm for unconstrained global optimization is developed from this new filled function. numerical results with a comparison on many test problems show that this algorithm is efficient and reliable. [ABSTRACT FROM AUTHOR]

In this paper we proposed reinforcement learning algorithms with the generalized reward function. In our proposed method we use Q-learning1 and SARSA1 algorithms with generalised reward function to train the reinforcement learning agent. We evaluated the performance of our proposed algorithms on Real Time Strategy (RTS) game called BattleCity. There are two main advantages of having such an approach as compared to other works in RTS. (1) We can ignore the concept of a simulator which is often game specific and is usually hard coded in any type of RTS games (2) our system can learn from interaction with any opponents and quickly change the strategy according to the opponents and do not need any human traces as used in previous works. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we introduce an algorithm to aggregate the preference relations provided by experts in multi-expert decision making problems. Instead of using a single aggregation function for the whole process, we start from a set of aggregation functions and select, by means of consensus done through penalty functions, the most suitable aggregation function in order to aggregate the individual preferences for each of the elements. An advantage of the method that we propose is that it allows us to recover the classical methods, just by using a single aggregation function. We also present a generalization of the concepts of restricted dissimilarity function and distance between sets for the case where we are working with a Cartesian product of lattices and use such concepts to build penalty functions. Finally, we propose an algorithm that allows us to choose the best combination of aggregation functions for a multi-expert decision making problem. [Copyright &y& Elsevier]

Abstract: Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization [Gandomi AH, Alavi AH. Krill Herd: A New Bio-Inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845, 2012.]. This paper represents an optimization method to global optimization using a novel variant of KH. This method is called the Stud Krill Herd (SKH). Similar to genetic reproduction mechanisms added to KH method, an updated genetic reproduction schemes, called stud selection and crossover (SSC) operator, is introduced into the KH during the krill updating process dealing with numerical optimization problems. The introduced SSC operator is originated from original Stud genetic algorithm. In SSC operator, the best krill, the Stud, provides its optimal information for all the other individuals in the population using general genetic operators instead of stochastic selection. This approach appears to be well capable of solving various functions. Several problems are used to test the SKH method. In addition, the influence of the different crossover types on convergence and performance is carefully studied. Experimental results indicate an instructive addition to the portfolio of swarm intelligence techniques. [Copyright &y& Elsevier]

Abstract: In this paper, the notion of two-direction vector-valued multiresolution analysis and the two-direction orthogonal vector-valued wavelets are introduced. The definition for two-direction orthogonal vector-valued wavelet packets is proposed. An algorithm for constructing a class of two-direction orthogonal vector-valued compactly supported wavelets corresponding to the two-direction orthogonal vector-valued compactly supported scaling functions is proposed by virtue of matrix theory and time–frequency analysis method. The properties of the two-direction vector-valued wavelet packets are investigated. At last, the direct decomposition relation for space L 2(R) r is presented. [Copyright &y& Elsevier]

Abstract: Since the empirical mode decomposition (EMD) lacks strict orthogonality, a new method for multicomponent signal decomposition, orthogonal empirical mode decomposition (OEMD), is proposed by this paper. The essential principle of this method is to obtain the intrinsic mode functions (IMFs) and the residue by self-adaptive band-pass filtering. Firstly, the feasibility of OEMD is theoretically analyzed, then its strict orthogonality and completeness is proved, and the orthogonal basis used in OEMD is generated. Secondly, the method of analytical band-pass filtering which preserves perfect band-pass feature in the frequency domain is presented, then two fast algorithms to implement OEMD are proposed, i.e. IMF sequential searching (ISS) algorithm and IMF binary searching (IBS) algorithm. The speed of IBS is faster than that of ISS, whereas IBS algorithm may obtain much more false IMFs than ISS when signals are of complex spectral constitutions. Finally, OEMD is applied to both synthetic signals and mechanical vibration signals, the results show that compared with EMD, OEMD better solves mode aliasing, avoids the occurrence of false mode, is free of end extension, and can be effectively applied to mechanical fault diagnosis. [Copyright &y& Elsevier]

Abstract: We consider a class of global optimization problems, in which the objective function is Lebesgue integrable. In this paper, we present a sufficient and necessary condition for computing the essential infimum. This optimality condition can be used to design algorithms for solving the global optimization problem. [Copyright &y& Elsevier]

Abstract: A new algorithm for calculating the two-dimensional differential transform of nonlinear functions is developed in this paper. This new technique is illustrated by studying suitable forms of nonlinearity. Three strongly nonlinear partial differential equations are then solved by differential transform method to demonstrate the validity and applicability of the proposed algorithm. The present framework offers a computationally easier approach to compute the transformed function for all forms of nonlinearity. This gives the technique much wider applicability. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new family of NCP-functions and the corresponding merit functions, which are the generalization of some popular NCP-functions and the related merit functions. We show that the new NCP-functions and the corresponding merit functions possess a system of favorite properties. Specially, we show that the new NCP-functions are strongly semismooth, Lipschitz continuous, and continuously differentiable; and that the corresponding merit functions have property (i.e., they are continuously differentiable and their gradients are semismooth) and property (i.e., they are continuously differentiable and their gradients are Lipschitz continuous) under suitable assumptions. Based on the new NCP-functions and the corresponding merit functions, we investigate a derivative free algorithm for the nonlinear complementarity problem and discuss its global convergence. Some preliminary numerical results are reported. [Copyright &y& Elsevier]

Abstract: In this paper, a new filled function which has better properties is proposed for identifying a global minimum point for a general class of nonlinear programming problems within a closed bounded domain. An algorithm for unconstrained global optimization is developed from the new filled function. Theoretical and numerical properties of the proposed filled function are investigated. The implementation of the algorithm on seven test problems is reported with satisfactory numerical results. [Copyright &y& Elsevier]

Abstract: A cultural algorithm with a differential evolution population is proposed in this paper. This cultural algorithm uses different knowledge sources to influence the variation operator of the differential evolution algorithm, in order to reduce the number of fitness function evaluations required to obtain competitive results. Comparisons are provided with respect to three techniques that are representative of the state-of-the-art in the area. The results obtained by our algorithm are similar (in quality) to those obtained by the other approaches with respect to which it was compared. However, our approach requires a lower number of fitness function evaluations than the others. [Copyright &y& Elsevier]

Abstract: In this paper, a kind of nonlinear optimization problems with nonlinear inequality constraints are discussed, and a new SQP feasible descent algorithm for solving the problems is presented. At each iteration of the new algorithm, a convex quadratic program (QP) which always has feasible solution is solved and a master direction is obtained, then, an improved (feasible descent) direction is yielded by updating the master direction with an explicit formula, and in order to avoid the Maratos effect, a height-order correction direction is computed by another explicit formula of the master direction and the improved direction. The new algorithm is proved to be globally convergent and superlinearly convergent under mild conditions without the strict complementarity. Furthermore, the quadratic convergence rate of the algorithm is obtained when the twice derivatives of the objective function and constrained functions are adopted. Finally, some numerical tests are reported. [Copyright &y& Elsevier]

Abstract: Given a finite set of nonnegative integers with no three-term arithmetic progressions, the Stanley sequence generated by , denoted as , is the infinite set created by beginning with and then greedily including strictly larger integers which do not introduce a three-term arithmetic progression in . Erdős et al. asked whether the counting function, , of a Stanley sequence satisfies for every and . In this paper we answer this question in the affirmative; in fact, we prove the slightly stronger result that for . [Copyright &y& Elsevier]