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Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs' resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study. [ABSTRACT FROM AUTHOR]

The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6 , depending on the number of thermal zones studied. [ABSTRACT FROM AUTHOR]

This paper continues formalization in the Mizar system [2, 1] of basic notions of the composition-nominative approach to program semantics [14] which was started in [8, 12, 10]. The composition-nominative approach studies mathematical models of computer programs and data on various levels of abstraction and generality and provides tools for reasoning about their properties. In particular, data in computer systems are modeled as nominative data [15]. Besides formalization of semantics of programs, certain elements of the composition-nominative approach were applied to abstract systems in a mathematical systems theory [4, 6, 7, 5, 3]. In the paper we give a formal definition of the notions of a binominative function over given sets of names and values (i.e. a partial function which maps simple-named complex-valued nominative data to such data) and a nominative predicate (a partial predicate on simple-named complex-valued nominative data). The sets of such binominative functions and nominative predicates form the carrier of the generalized Glushkov algorithmic algebra for simple-named complex-valued nominative data [15]. This algebra can be used to formalize algorithms which operate on various data structures (such as multidimensional arrays, lists, etc.) and reason about their properties. In particular, we formalize the operations of this algebra which require a specification of a data domain and which include the existential quantifier, the assignment composition, the composition of superposition into a predicate, the composition of superposition into a binominative function, the name checking predicate. The details on formalization of nominative data and the operations of the algorithmic algebra over them are described in [11, 13, 9]. [ABSTRACT FROM AUTHOR]

This paper continues formalization in Mizar [2, 1] of basic notions of the composition-nominative approach to program semantics [13] which was started in [8, 11]. The composition-nominative approach studies mathematical models of computer programs and data on various levels of abstraction and generality and provides tools for reasoning about their properties. Besides formalization of semantics of programs, certain elements of the composition-nominative approach were applied to abstract systems in a mathematical systems theory [4, 6, 7, 5, 3]. In the paper we introduce a definition of the notion of a binominative function over a set D understood as a partial function which maps elements of D to D. The sets of binominative functions and nominative predicates [11] over given sets form the carrier of the generalized Glushkov algorithmic algebra [14]. This algebra can be used to formalize algorithms which operate on various data structures (such as multidimensional arrays, lists, etc.) and reason about their properties. We formalize the operations of this algebra (also called compositions) which are valid over uninterpretated data and which include among others the sequential composition, the prediction composition, the branching composition, the monotone Floyd-Hoare composition, and the cycle composition. The details on formalization of nominative data and the operations of the algorithmic algebra over them are described in [10, 12, 9]. [ABSTRACT FROM AUTHOR]

The clonal selection algorithm(CSA) is a core method in artificial immune system, which is famous for its intelligent evolution in artificial intelligence application. However, There are some shortcomings in the algorithm, such as local optima and low convergence speed, which make its practical effects not ideal. Culture algorithm(CA) is driven by knowledge, which can significantly improve the evolutionary efficiency. Chaos mechanism can make the algorithm have better problem space coverage ability. Therefore, a culture&chaos-inspired CSA(CC-CSA) is proposed in this paper to deal with the problems mentioned before. CC-CSA adopts the double-layer evolutionary framework of CA to extract knowledge and guide the crossover and chaotic mutation operation to complete the evolution process. The implicit knowledge is used to adaptively control the chaotic mutation scale, guide the individuals to jump out of the local optima, and realize the accurate search in the latter evolution cycle to gradually approach the optimal solution. It can be seen from the mathematical model analysis that CC-CSA can converge to the global optimal solution. Compared with the experimental results of the original CSA and its representative, up-to-date improved methods, CC-CSA has the fastest convergence speed and the best detection performances. It is also proved that CC-CSA can solve the problems of local optima and slow convergence speed by using the knowledge guidance of CA's double-layer framework and good coverage ability of chaos mechanism to the problem space. [ABSTRACT FROM AUTHOR]

In this paper, we prove that for a linearly ordered skewfield the groups of quotients of the semigroup Gn(픻) coincides with the group GLn(픻) for n ≥ 3. [ABSTRACT FROM AUTHOR]

In this paper a problem of specifying HIV-infection parameters and immune response using additional measurements of the concentrations of the T-lymphocytes, the free virus and the immune effectors at fixed times for a mathematical model of HIV dynamics is investigated numerically. The problem of the parameter specifying of the mathematical model (an inverse problem) is reduced to a problem of minimizing an objective function describing the deviation of the simulation results from the experimental data. A genetic algorithm for solving the least squares function minimization problem is implemented and investigated. The results of a numerical solution of the inverse problem are analyzed. [ABSTRACT FROM AUTHOR]

In this paper, we consider the discrete unit square cover (DUSC) problem as follows: given a set 𝒫 of n points and a set 𝒮 of m axis-aligned unit squares in ℝ 2 , the objective is (i) to check whether the union of the squares in 𝒮 covers all the points in 𝒫 , and (ii) if the answer is yes, then select a minimum cardinality subset 𝒮 ∗ ⊆ 𝒮 such that each point in 𝒫 is covered by at least one square in 𝒮 ∗ . For the DUSC problem: (i) we propose a (2 + 4 k − 2) -approximation algorithm, where k (> 2) is an integer parameter that defines a trade-off between the running time and the approximation factor of the algorithm. The running time of our proposed algorithm is O (k m k n + n log n). Our solution of the DUSC problem is based on a simple (1 + 2 k − 2) -approximation algorithm for the subproblem strip square cover (SSC) problem, where all the points in 𝒫 are lying within a horizontal strip of unit height. (ii) we also propose a 2-approximation algorithm, which runs in O (m 4 n) time. The 2-approximation algorithm is based on an algorithm for the subproblem SSC problem. The algorithm for the subproblem is developed using plane sweep and graph search traversal techniques. We also extend this algorithm to get 2-approximation result for the weighted DUSC problem where the squares are assigned weights, and the aim is to choose a subset 𝒮 ∗ ⊆ 𝒮 such that each point in 𝒫 is covered by at least one square in 𝒮 ∗ and the sum of the weights of squares in 𝒮 ∗ is minimized. [ABSTRACT FROM AUTHOR]

The conditional diagnosis is a very important measure of the reliability and the fault-tolerance of networks. The ‘condition’ means that no faulty set contains all neighbours of any node. Under this assumption, for any systemG, every component ofhas more than 1 node, whereFis the faulty set ofG. Theg-extra conditional diagnosability is defined under the assumption that every component ofhas more thannodes. ‘A system with at mosttfaulty nodes is defined as sequentiallyt-diagnosable if at least one faulty node can be repaired, so that the testing can be continued using the repaired node to eventually diagnose all faulty nodes’ [E.P. Duarte Jr., R.P. Ziwich, and L.C.P. Albini,A survey of comparison-based system-level diagnosis, ACM Comput. Surv. 43(3) (2011), article 22]. To increase the degree of the sequentialt-diagnosability of a system, sequential-diagnosis strategy is proposed in this paper. It is allowed that there are at mostkmisdiagnosed nodes. In this paper, we determine theg-extra conditional diagnosability of hypercubes and propose sequential-diagnosis algorithms for hypercubes with low time complexities under the Preparata, Metze, and Chien (PMC) model and the MM* model which is a special case of the Maeng and Malek (MM) model. [ABSTRACT FROM PUBLISHER]

In the past decade the humanitarian supply chain (HSC) has attracted the attention of researchers due to the increasing frequency of disasters. The uncertainty in time, location, and severity of disaster during predisaster phase and poor conditions of available infrastructure during postdisaster phase make HSC operations difficult to handle. In order to overcome the difficulties during these phases, we need to assure that HSC operations are designed in an efficient manner to minimize human and economic losses. In the recent times, several mathematical optimization techniques and algorithms have been developed to increase the efficiency of HSC operations. These techniques and algorithms developed for the field of HSC motivate the need of a systematic literature review. Owing to the importance of mathematical modelling techniques, this paper presents the review of the mathematical contributions made in the last decade in the field of HSC. A systematic literature review methodology is used for this paper due to its transparent procedure. There are two objectives of this study: the first one is to conduct an up-to-date survey of mathematical models developed in HSC area and the second one is to highlight the potential research areas which require attention of the researchers. [ABSTRACT FROM AUTHOR]

This paper presents a review of the most relevant current techniques that deal with hole-filling in 3D models. Contrary to earlier reports, which approach mesh repairing in a sparse and global manner, the objective of this review is twofold. First, a specific and comprehensive review of hole-filling techniques (as a relevant part in the field of mesh repairing) is carried out. We present a brief summary of each technique with attention paid to its algorithmic essence, main contributions and limitations. Second, a solid comparison between 34 methods is established. To do this, we define 19 possible meaningful features and properties that can be found in a generic hole-filling process. Then, we use these features to assess the virtues and deficiencies of the method and to build comparative tables. The purpose of this review is to make a comparative hole-filling state-of-the-art available to researchers, showing pros and cons in a common framework. [ABSTRACT FROM AUTHOR]

The Euler genus of a graph is a fundamental and well-studied parameter in graph theory and topology. Computing it has been shown to be NP-hard by Thomassen [J. Algorithms, 10 (1989), pp. 568{576; J. Combin. Theory, Ser. B, 57 (1993), pp. 196{206], and it is known to be fixed-parameter tractable. However, the approximability of the Euler genus is wide open. While the existence of an O(1)-approximation is not ruled out, only an O( p n)-approximation [J. Chen, S. P. Kanchi, and A. Kanevsky, Inform. Process. Lett., 61 (1997), pp. 317{322] is known even in bounded-degree graphs. In this paper we give a polynomial-time algorithm which, given a boundeddegree graph of Euler genus g, computes a drawing in a surface of Euler genus gO(1) · logO(1) n. Combined with the upper bound from [J. Chen, S. P. Kanchi, and A. Kanevsky, Inform. Process. Lett., 61 (1997), pp. 317{322], our result also implies a O(n1/2-α)-approximation for some constant α > 0. Using our algorithm for approximating the Euler genus as a subroutine, we obtain, in a uniform fashion, algorithms with approximation ratios of the form OPTO(1) · logO(1) n for several related problems on bounded-degree graphs. These include the problems of orientable genus, crossing number, and planar edge and vertex deletion. Our algorithm and proof of correctness for the crossing number problem are simpler compared to the long and difficult proof in the recent breakthrough by Chuzhoy [Proceedings of the ACM Symposium on Theory of Computing, 2011, pp. 303{312], while essentially obtaining a qualitatively similar result. For planar edge and vertex deletion problems our results are the first to obtain a bound of the form poly(OPT; log n). We also highlight some further applications of our results in the design of algorithms for graphs with small genus. Many such algorithms require that a drawing of the graph is given as part of the input. Our results imply that in several interesting cases, we can implement such algorithms even when the drawing is unknown. [ABSTRACT FROM AUTHOR]

The block inverse iteration and block Newton’s methods proposed by the authors of this paper for computing invariant pairs of regular linear matrix pencils are generalized to the case of regular nonlinear matrix pencils. Numerical properties of the proposed methods are demonstrated with a typical quadratic eigenproblem. [ABSTRACT FROM AUTHOR]

Music plays a prominent role in society and companies have even started studying its aspects for commercial purposes. It is only natural to ask what characteristics make certain songs appealing. While much research has been conducted on the mathematical principles of sound, there has been less focus on analyzing the structure of popular songs from a mathematical perspective. One mathematical tool that researchers have used to study musical structure is seriation, ordering. This paper applies several types of seriation algorithms to conduct a mathematical analysis of the structural qualities of several musical pieces. This paper focuses on 10 popular artists and their musical influences. The artists chosen for this research are linked because of the influences they cite, musical genre, and the popularity of their music. Results show that an artist's songs have a higher quantitatively measured connection with the artists they cite as influences rather than the artists who they never mention as musical influences. [ABSTRACT FROM AUTHOR]

In this paper, the duality in differential form is developed between a 3D primal surface and its dual manifold formed by the surface's tangent planes, that is, each tangent plane of the primal surface is represented as a four-dimensional vector that constitutes a point on the dual manifold. The iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3D surface, that is, the "dual" of the "dual" goes back to the "primal." This theorem can be directly used to reconstruct 3D surface from image edges by estimating the dual manifold from these edges. In this paper, we further develop the work in our original conference papers resulting in the robust differential dual operator. We argue that the operator makes good use of the information available in the image data by using both points of intensity discontinuity and their edge directions; we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy; our algorithm operates on all edges in the images, including silhouette edges, self occlusion edges, and texture edges, without distinguishing their types (thus, resulting in improved accuracy and handling locally concave surface estimation if texture edges are present); the algorithm automatically handles various degeneracies; and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images, evaluating and using the algorithm's sensitivity to noise to determine the accuracy of an estimated 3D point. Experiments with both synthetic and real images demonstrate that the operator is accurate, robust to degeneracies and noise, and general for reconstructing free-form objects from occluding edges and texture edges detected in calibrated images or video sequences. [ABSTRACT FROM AUTHOR]

The article presents information related to the periodical that focuses on mathematics. Topics discussed include declaration on equal opportunities, diversity and scientific collaboration; new trends in algorithmic randomness and computable analysis; nonlocal conservation laws; mathematical models; and mathematical analysis and quantum systems.

This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as Bagging, Boosting, Random Forests, and many distributed and data stream mining algorithms. Orthogonal decision trees are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. This paper offers a technique to construct such trees based on the Fourier transformation of decision trees and eigen-analysis of the ensemble in the Fourier representation. It offers experimental results to document the performance of orthogonal trees on the grounds of accuracy and model complexity. [ABSTRACT FROM AUTHOR]

This paper describes a Boolean satisfiability checking (SAT)-based unbounded symbolic model-checking algorithm. The conjunctive normal form is used to represent sets of states and transition relation. A logical operation on state sets is implemented as an operation on conjunctive normal form formulas. A satisfy-all procedure is proposed to compute the existential quantification required in obtaining the preimage and fix point. The proposed satisfy-all procedure is implemented by modifying a SAT procedure to generate all the satisfying assignments of the input formula, which is based on new efficient techniques such as line justification to make an assignment covering more search space, excluding clause management, and two-level logic minimization to compress the set of found assignments. In addition, a cache table is introduced into the satisfy-all procedure. It is a difficult problem for a satisfy-all procedure to detect the case that a previous, result can be reused. This paper shows that the case can be detected by comparing sets of undetermined variables and clauses. Experimental results show that the proposed algorithm can check more circuits than binary decision diagram-based and previous SAT-based model-checking algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we first propose a weak convergence algorithm, called the linesearch algorithm, for solving a split equilibrium problem and nonexpansive mapping (SEPNM) in real Hilbert spaces, in which the first bifunction is pseudomonotone with respect to its solution set, the second bifunction is monotone, and fixed point mappings are nonexpansive. In this algorithm, we combine the extragradient method incorporated with the Armijo linesearch rule for solving equilibrium problems and the Mann method for finding a fixed point of an nonexpansive mapping. We then combine the proposed algorithm with hybrid cutting technique to get a strong convergence algorithm for SEPNM. Special cases of these algorithms are also given. [ABSTRACT FROM AUTHOR]

This paper is developed to the study on the general properties of the relation between the orlicz space of χMπ and χMπ (a). [ABSTRACT FROM AUTHOR]

This paper proposes a new stochastic method based on u-transform (UT) to capture the uncertainties of the reconfiguration in a correlated environment. The proposed stochastic framework makes use of the idea covariance matrix to reach this goal. Also, in order to reach the maximum efficiency of the reconfiguration strategy, reliability indices are considered along with the traditional targets in the multi-objective framework. Therefore, the objective functions to be optimized are System Average Interruption Frequency Index (SAIFI), Average Energy Not Supplied (AENS), total active power losses and the total network costs. Since the problem investigated is a complex, nonlinear optimization problem, a new optimization method based on krill herd algorithm (KH) is introduced. In addition, a new modification method is proposed for the KH algorithm to improve its total search ability. The feasibility and performance of the proposed method are examined on the IEEE standard test system. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to study the proximal split feasibility problems. For solving the problems, we present new self-adaptive algorithms with the regularization technique. By using these algorithms, we give some strong convergence theorems for the proximal split feasibility problems. [ABSTRACT FROM AUTHOR]

This paper presents seven theorems which expand understanding of the theoretical structure of the Charnes­Cooper­Rhodes (CCR) model of Data Envelopment Analysis, especially with respect to slacks and the underlying structure of facets and faces. These theorems also serve as a basis for new algorithms which will provide optimal primal and dual solutions that satisfy the strong complementary slackness conditions (SCSC) for many (if not most) non-radially efficient DMUs; an improved procedure for identifying the set E of extreme efficient DMUs; and may, for many DEA domains, also settle in a single pass the existence or non-existence of input or output slacks in each of their DMUs. This paper also introduces the concept of a positive goal vector G, which is applied to characterize the set of all possible maximal optimal slack vectors. The appendix C presents an example which illustrates the need for a new concept, face regular, which focuses on the role of convexity in the intersections of radial efficient facets with the efficient frontier FR. The same example also illustrates flaws in the popular ‘sum of the slacks’ methodology. [ABSTRACT FROM AUTHOR]

In this paper, finite-time stability and stabilization problems for a class of linear stochastic systems are studied. First, a new concept of finite-time stochastic stability is defined for linear stochastic systems. Then, based on matrix inequalities, some sufficient conditions under which the stochastic systems are finite-time stochastically stable are given. Subsequently, the finite-time stochastic stabilization is studied and some sufficient conditions for the existence of a state feedback controller and a dynamic output feedback controller are presented by using a matrix inequality approach. An algorithm is given for solving the matrix inequalities arising from finite-time stochastic stability (stabilization). Finally, two examples are employed to illustrate the results. [ABSTRACT FROM AUTHOR]

In this paper, we analyze a collaborative filter that answers the simple question: What is popular amongst your “friends”? While this basic principle seems to be prevalent in many practical implementations, there does not appear to be much theoretical analysis of its performance. In this paper, we partly fill this gap. While recent works on this topic, such as the low-rank matrix completion literature, consider the probability of error in recovering the entire rating matrix, we consider probability of an error in an individual recommendation [bit error rate (BER)]. For a mathematical model introduced by Aditya in 2009 and 2011, we identify three regimes of operation for our algorithm (named Popularity Amongst Friends) in the limit as the matrix size grows to infinity. In a regime characterized by large number of samples and small degrees of freedom (defined precisely for the model in the paper), the asymptotic BER is zero; in a regime characterized by large number of samples and large degrees of freedom, the asymptotic BER is bounded away from 0 and 1/2 (and is identified exactly except for a special case); and in a regime characterized by a small number of samples, the algorithm fails. We then compare these results with the performance of the optimal recommender. We also present numerical results for the MovieLens and Netflix datasets. We discuss the empirical performance in light of our theoretical results and compare with an approach based on low-rank matrix completion. [ABSTRACT FROM AUTHOR]

Quasi-subtractive varieties are a generalization of subtractive varieties, introduced to account for some correspondence theorems between ideals and congruences in the literature that are not corollaries of general theorems in the theory of subtractive varieties. The main tool for the investigation of quasi-subtractive varieties is the concept of open filter, that extends the notion of ideal due to Gumm and Ursini. In this paper, we explore further the fundamental properties of open filters and congruences in this context; moreover, we introduce and investigate a notion of commutator of open filters. [ABSTRACT FROM AUTHOR]

A discrete element code with impact model has been developed and calibrated to simulate the dynamic behavior of rock materials, with special regard to rock fragmentation upon impact during rock-fall analysis. The paper summarizes the discrete element code, the calibration algorithms developed to identify the model microparameters, and the impact model. Experimental work on drop tests is then used to validate the code on modeling impact fragmentation. It has been found that the developed discrete element code and impact model can reasonably simulate rock fragmentation in drop tests. The use of the discrete element code and impact model can provide good reference results in evaluating impact fragmentation in rock-fall analysis. [ABSTRACT FROM AUTHOR]

Boltzmann models from statistical physics, combined with methods from analytic combinatorics, give rise to efficient and easy-to-write algorithms for the random generation of combinatorial objects. This paper proposes to extend Boltzmann generators to a new field of applications by uniformly sampling a Hadamard product. Under an abstract real-arithmetic computation model, our algorithm achieves approximate-size sampling in expected time ${\cal O}$(n${\sqrt n}$) or ${\cal O}$(nσ) depending on the objects considered, with σ the standard deviation of smallest order for the component object sizes. This makes it possible to generate random objects of large size on a standard computer. The analysis heavily relies on a variant of the so-called birthday paradox, which can be modelled as an occupancy urn problem. [ABSTRACT FROM AUTHOR]

System level diagnosis is an important technique for fault detection and location in multiprocessor computing systems. Adaptive diagnosis, proposed by Nakajima, is one of many practical approach system level diagnostic schemes. As far as we know, the adaptive approach under the MM model has only been discussed in relation to a completely connected system. In this paper, we consider the problem of adaptive fault diagnosis for systems modelled by a cycle and a torus under the MM model. For cycles, we give some useful properties for identifying faulty vertices, show the minimum number of test rounds and provide some efficient test assignments. We also present two adaptive diagnosis algorithms for tori and show the minimum number of tests for these algorithms. Moreover, the two algorithms take linear time both for overall testing and syndrome decoding. [ABSTRACT FROM PUBLISHER]

In this paper I make an attempt to describe, discuss and extend a few aspects of the rich mathematical tapestry that can be woven with rigorous notions of non-linear dynamics, complexity and randomness, in terms of algorithmic mathematics. It is a tapestry that I try to weave with economic analysis, economic theory and economic modelling in mind. All three notions - that is, non-linear dynamics, complexity and randomness - have a rich conceptual, modelling or analytic tradition in core areas of economic theory, both at the micro and macro levels. It is the algorithmic foundation I try to provide for them that could be considered the novel contribution in this paper. Once the algorithmic foundations are in place, it is, for example, almost natural to consider the famed difficulties of obtaining closed form solutions for non-linear, complex or random dynamic models in economics almost a trivial vestige of a pre-simulation era in mathematical modelling. [ABSTRACT FROM AUTHOR]

Preemptive scheduling often generates a significant runtime overhead that may increase task worst-case execution times up to 40%, with respect to a fully non-preemptive execution. In small embedded systems, such an extra cost results in longer and more variable response times that can significantly affect the overall energy consumption, as well as the system predictability. Limiting preemptions is often possible without jeopardizing schedulability. Although several authors addressed schedulability analysis under different forms of limited preemptive scheduling, current results exhibit two major deficiencies: (i) The maximum lengths of the non-preemptive regions for each task are still unknown under fixed priorities; (i) The exact response time analysis for tasks with fixed preemption points is too complex. This paper presents the schedulability analysis of real-time tasks with non-preemptive regions, under fixed priority assignments. In particular, two different preemption models are considered: the floating and the fixed preemption point model. Under each model, the feasibility analysis is addressed by deriving simple and effective schedulability tests, as well as an algorithm for computing the maximum length of the non-preemptive regions for each task. Finally, simulation experiments are presented to compare the two models in terms of schedulability. [ABSTRACT FROM AUTHOR]

This paper presents a developed algorithm for reliability sensitivity analysis of engineering networks. . Reliability Modeling is proposed for the Iraqi Kurdistan Regional Power Network (IKRPN) using Symbolic Reliability function of the model. The written Pascal code for the developed algorithm finds efficiently path sets and cut sets of the model. Reliability and Unreliability indices are found. The sensitivity of these indices are found with respect to the variation of the network's elements reliabilities [ABSTRACT FROM AUTHOR]

This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes. Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample. [ABSTRACT FROM AUTHOR]

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing. [ABSTRACT FROM AUTHOR]

The GDPO algorithm for phase-1 of the dual simplex method developed by Maros possesses some interesting theoretical features that have potentially huge computational advantages. This paper gives account of a computational analysis of GDPO that has investigated how these features work in practice by exploring the internal operation of the algorithm. Experience of a systematic study involving 48 problems gives an insight how the predicted performance advantages materialize that ultimately make GDPO an indispensable tool for dual phase-1. [ABSTRACT FROM AUTHOR]

Reliability-Based impact (R-impact) factor, defined as the cited half-life multiplied by the citation impact factor, measures both the citation impact and long-lasting impact of published journals. Currently there are several different ways to calculate the citation rates, each with limitations to be improved. This paper provides a new analysis approach for the ranking and citation of published journals. Because cited half-life is the number of publication years from the current year that account for 50% of the current citations published by a journal in its article references, it can evaluate the age of the majority of cited articles. Through using the cited half-life as the sample time variable, we can seek the normalized value of citations and the citable items. This value can avoid the impacts from different journals or different fields. A more radical improvement is suggested in the impact factor, and relative impact factor, which can measure effectively not only the short-time performance of the journals but also the long-time performance. Based on this, relative R-impact (RRI) is proposed to improve R-impact effectively. [ABSTRACT FROM AUTHOR]

Graph analysis is playing an increasingly important role in science and industry. Due to numerous limitations in sharing real-world graphs, models for generating massive graphs are critical for developing better algorithms. In this article, we analyze the stochastic Kronecker graph model (SKG), which is the foundation of the Graph500 supercomputer benchmark due to its favorable properties and easy parallelization. Our goal is to provide a deeper understanding of the parameters and properties of this model so that its functionality as a benchmark is increased. We develop a rigorous mathematical analysis that shows this model cannot generate a power-law distribution or even a lognormal distribution. However, we formalize an enhanced version of the SKG model that uses random noise for smoothing. We prove both in theory and in practice that this enhancement leads to a lognormal distribution. Additionally, we provide a precise analysis of isolated vertices, showing that the graphs that are produced by SKG might be quite different than intended. For example, between 50% and 75% of the vertices in the Graph500 benchmarks will be isolated. Finally, we show that this model tends to produce extremely small core numbers (compared to most social networks and other real graphs) for common parameter choices. [ABSTRACT FROM AUTHOR]

We present a new computational approach to the problem of placing n identical nonoverlapping disks in the unit square in such a way that their radii are maximized. The problem has been studied in a large number of papers, from both a theoretical and a computational point of view. In this paper, we conjecture that the problem possesses a so-called funneling landscape, a feature that is commonly found in molecular conformation problems. Based on this conjecture, we develop a stochastic search algorithm that displays excellent numerical performance. Thanks to this algorithm, we could improve over previously known putative optima in the range n ≤ 130 in as many as 32 instances, the smallest of which is n=53. [ABSTRACT FROM AUTHOR]

Motivated by an important problem of load balancing in parallel computing, this paper examines a modified algorithm to enhance Q-learning methods, especially in asynchronous recursive procedures for self-adaptive load distribution at runtime. Unlike the existing projection method that utilizes a fixed region, our algorithm employs a sequence of growing truncation bounds to ensure the boundedness of the iterates. Convergence and rates of convergence of the proposed algorithm are established. This class of algorithms has broad applications in signal processing, learning, financial engineering, and other related fields. [ABSTRACT FROM AUTHOR]

Path integral algorithms are developed for evaluating European-style Asian options within three new families of multi-parameter local volatility models. The forward price process is a martingale under an assumed risk-neutral measure and the transition probability densities for such models are given in analytically closed form. Implied volatility surfaces for these models exhibit a wide range of pronounced smiles and skews of the type observed in the option markets. By concatenating the pricing kernels in time, we obtain an exact multidimensional path integral representation for any discrete-time path-dependent option on these models. Nonlinear variable transformations and numeraire changes are exploited in simplifying the path integrals. Efficient weighted Monte Carlo schemes for accurate evaluation of the integrals are then presented. The algorithms combine variance reduction techniques such as the control variate method with distributed computing to significantly increase efficiency. Our numerical results show that distributed Monte Carlo simulations scale almost linearly with the increase in independent processors on a high-performance computer cluster. As a competitive approach we develop a multinomial lattice method that is based on the backward propagation of the option value function using an updating rule. The paper includes simulation results for two kinds of Asian options: average price and average strike options. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm which, for a given compact orientable irreducible boundary irreducible 3-manifold M, verifies whether M contains an essential orientable surface (possibly, with boundary), whose genus is at most N. The algorithm is based on Haken’s theory of normal surfaces, and on a trick suggested by Jaco and consisting in estimating the mean length of boundary curves in an unknown essential surface of a given genus in the given manifold. [ABSTRACT FROM AUTHOR]

Let F be a function on pairs of vertices. An F-labeling scheme is composed of a marker algorithm for labeling the vertices of a graph with short labels, coupled with a decoder algorithm allowing one to compute F( u, v) for any two vertices u and v directly from their labels. As applications for labeling schemes concern mainly large and dynamically changing networks, it is of interest to study distributed dynamic labeling schemes. This paper investigates labeling schemes for dynamic trees. We consider two dynamic tree models, namely, the leaf-dynamic tree model in which at each step a leaf can be added to or removed from the tree and the leaf-increasing tree model in which the only topological event that may occur is that a leaf joins the tree. A general method for constructing labeling schemes for dynamic trees (under the above mentioned dynamic tree models) was previously developed in Korman et al. (Theory Comput Syst 37:49–75, 2004). This method is based on extending an existing static tree labeling scheme to the dynamic setting. This approach fits many natural functions on trees, such as distance, separation level, ancestry relation, routing (in both the adversary and the designer port models), nearest common ancestor etc.. Their resulting dynamic schemes incur overheads (over the static scheme) on the label size and on the communication complexity. In particular, all their schemes yield a multiplicative overhead factor of Ω(log n) on the label sizes of the static schemes. Following (Korman et al., Theory Comput Syst 37:49–75, 2004), we develop a different general method for extending static labeling schemes to the dynamic tree settings. Our method fits the same class of tree functions. In contrast to the above paper, our trade-off is designed to minimize the label size, sometimes at the expense of communication. Informally, for any function k( n) and any static F-labeling scheme on trees, we present an F-labeling scheme on dynamic trees incurring multiplicative overhead factors (over the static scheme) of $$O(\log_{k(n)} n)$$ on the label size and $$O(k(n)\log_{k(n)} n)$$ on the amortized message complexity. In particular, by setting $$k(n) = n^{\epsilon}$$ for any $$0 < \epsilon < 1$$ , we obtain dynamic labeling schemes with asymptotically optimal label sizes and sublinear amortized message complexity for the ancestry relation, the id-based and label-based nearest common ancestor relation and the routing function. [ABSTRACT FROM AUTHOR]

This paper extends recent work on finding the stochastic queue median (SQM) on a network. The SQM is the optimal location of a single mobile server, when idle, who travels to customers in response to requests for service. Customer demands are limited to the nodes of the network and their requests for service arrive according to a time homogeneous Poisson process, independently from each node. When a service request finds the server busy with a previous customer, it is entered into an M/G/1 queue that is depleted in a first-in, first-out (FIFO) manner. The SQM is the point on the network at which the sum of mean queueing delay and mean travel time is minimized. In this paper the transportation network is restricted to be a tree. By discovering and exploiting convex properties of the objective function and related functions, an efficient finite-step algorithm is found for locating the SQM on a tree. [ABSTRACT FROM AUTHOR]

Let T = (V,E) be an edge-weighted tree with &verbar;V&verbar; = n vertices embedded in the Euclidean plane. Let IE denote the set of all points on the edges of T. Let X and Y be two subsets of IE and let r be a positive real number. A subset D ⊆ X is an X/Y/r-dominating set if every point in Y is within distance r of a point in D. The X/Y/r-dominating set problem is to find an X/Y/r-dominating set D* with minimum cardinality. Let p ≥ 1 be an integer. The X/Y/p-center problem is to find a subset C* ⊆ X of p points such that the maximum distance of any point in Y from C* is minimized. Let X and Y be either V or IE. In this paper, efficient parallel algorithms on the EREW PRAM are first presented for the X/Y/r-dominating set problem. The presented algorithms require O(log² n) time for all cases of X and Y. Parallel algorithms on the BREW PRAM are then developed for the X/Y/p-center problem. The presented algorithms require O(log³ n) time for all cases of X and Y. Previously, sequential algorithms for these two problems had been extensively studied in the literature. However, parallel solutions with polylogarithmic time existed only for their special cases. The algorithms presented in this paper are obtained by using an interesting approach which we call the dependency-tree approach. Our results are examples of parallelizing sequential dynamic-programming algorithms by using the approach. [ABSTRACT FROM AUTHOR]

The paper presents a new approach to solving a class of combinatorial economic allocation problems. One member of this class is known as the quadratic assignment problem. Besides presenting an algorithm to solve this problem, we will discuss in general terms the techniques for treating combinatorial problems. A novel feature of the paper is the development of the statistical properties of the criterion function. These statistical properties are used in conjunction with a general enumerative procedure to form the main parts of the algorithm. Using the idea of confidence level enumeration, an extension of the algorithm is proposed which should allow for the effective treatment of large scale combinatorial problems. Finally we present some computational results in order to illustrate the effectiveness of the general approach. [ABSTRACT FROM AUTHOR]

In this paper we consider the following problem: given a finite set of straight-line segmentsSin ℝ2, find minimum in size setVof points on the segments, such that each segment ofScontains at least one point inV. We call this problem guarding a set of segments (GSS). GSS is a special case of the set cover problem where the given family of subsets can be taken as a set of intersections of the straight-line segments inS. Requiring that the given subsets can be interpreted geometrically this way is a major restriction on the input, yet it has been shown that the problem is still strongly NP-complete [V.E. Brimkov, A. Leach, M. Mastroianni, and J. Wu,Guarding a set of line segments in the plane, Theor. Comput. Sci. 412 (2011), pp. 1313–1324]. In light of this result, in Brimkovet al.[Experimental studies on approximation algorithms for guarding sets of line segments, inAdvances in Visual Computing, G. Bebis, R. Boyle, B. Parvin, D. Koracin, R. Chung, R. Hammoud, M. Hussain, T. Kar-Han, R. Crawfis, D. Thalmann, D. Kao, and L. Avila, eds., ISVC 2010, Part I, Lecture Notes in Computer Science, Vol. 6453, Springer, Berlin, 2010, pp. 592–601; V.E. Brimkov, A. Leach, M. Mastroianni, and J. Wu,Approximation algorithms for a geometric set cover problem, Discrete Appl. Math. 160 (2012), pp. 1039–1052] the GSS approximability was studied both theoretically and experimentally. Here we continue these investigations. In particular, we obtain conditions under which GSS admits good approximation. [ABSTRACT FROM AUTHOR]

This paper reports on enhancing the thermal performance of multiple-chip modules (MCMs) that contain a number of chips under natural convection by optimizing the chip placement layout. To attain this goal, an innovative hybrid optimization approach (HOA) incorporating a genetic algorithm (GA) into an algorithm based on a response surface method (RSM) is introduced for improving the performance of both these algorithms. The GA in the proposed HOA is responsible for not only evolving the population toward better fitness value but also, based on the newly evolved populations at each GA generation, for continuously updating the RS mathematical model for better approximation of the chip junction temperature. The sum of the mathematical expressions representing the total system temperature defines the objective of the optimization problems. For each GA generation, a constrained quadratic optimization subproblem is formed, based on the newly updated approximate RS mathematical model as the objective function along with the specified constraints. The solution of the optimization subproblem is sought through a mathematical programming model. As the genetic RS optimization progresses, a sequence of approximate solutions associated with the continuously updated RS mathematical models is constructed. The iterative process continues until convergence of the approximate solutions is attained. To demonstrate the effectiveness of the developed algorithm, several thermal design problems associated with two types of MCMs with equal/unequal power are performed. The obtained results are compared with those derived using two conventional approaches—the GA-based and the RSM-based optimization techniques. Results show that the developed algorithm can provide good optimal solutions with much less computational effort, and the larger the scale of the design problems, the more significant the improvement in the computation cost. [ABSTRACT FROM PUBLISHER]

Purchasing managers face a complex problem when determining order quantities for multiple items in the presence of quantity discounts and resource limitations. In this paper, the problem is formulated as a mathematical programming model. An efficient solution algorithm is developed utilizing the Lagrangian relaxation approach. Extensive computational experiments are performed and the results are presented. [ABSTRACT FROM AUTHOR]