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In problems involving control of financial processes, it is usually complicated to quantify exactly the state variables. It could be expensive to acquire the exact value of a given state, even if it may be physically possible to do so. In such cases it may be interesting to support the decision-making process on inaccurate information pertaining to the system state. In addition, for modeling real-world application, it is necessary to compute the values of the parameters of the environment (transition probabilities and observation probabilities) and the reward functions, which are typically, hand-tuned by experts in the field until it has acquired a satisfactory value. This results in an undesired process. To address these shortcomings, this paper provides a new Reinforcement Learning (RL) framework for computing the mean-variance customer portfolio with transaction costs in controllable Partially Observable Markov Decision Processes (POMDPs). The solution is restricted to finite state, action, observation sets and average reward problems. For solving this problem, a controller/actor-critic architecture is proposed, which balance the difficult tasks of exploitation and exploration of the environment. The architecture consists of three modules: controller, fast-tracked portfolio learning and an actor-critic module. Each module involves the design of a convergent Temporal Difference (TD) learning algorithm. We employ three different learning rules to estimate the real values of: (a) the transition matrices π ^ (j | i k) (t) , (b) the rewards J ^ (i j k) (t) and (c) the resources destined for carrying out a promotion ζ ^ (i ^ | k ^) (t). We present a proof for the estimated transition matrix rule π ^ (j | i k) (t) and showing that it converges when t → ∞. For solving the optimization programming problem we extend the c-variable method for partially observable Markov chains. The c-variable is conceptualized as joint strategy given by the product of the control policy, the observation kernel Q(y|s) and the stationary distribution vector. A major advantage of this procedure is that it can be implemented efficiently for real settings in controllable POMDP. A numerical example illustrates the results of the proposed method. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We propose an ensemble selector. • Multi-fitness function is considered. • Our approach can improve stability of reduct. Abstract Through abstracting commonness from the existing heuristic algorithms, control strategies bring us higher level understandings of building reducts in rough set theory. To further improve the performances and strengthen the applicabilities of the addition control strategy, an ensemble selector is introduced into such framework. This ensemble selector is realized through using a set of the fitness functions which may be constructed by homogenous or heterogeneous evaluations of the candidate attributes. Based on the neighborhood rough set model, the experimental results tell us that by comparing the traditional addition control strategy, ensemble selector is effective in improving the stabilities of the reducts, the stabilities of the classification results and the AUC values from the viewpoints of KNN and SVM classifiers. This study suggests new trends for considering attribute reduction problems and provides guidelines for designing new algorithms in rough set theory. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Designing the evaluation function to measure the effect of temperature parameter. • Deriving the effective updating rule to determine the optimal temperature parameter. • Applying Sotmax function with optimal parameter to handle D-armed bandit problems. Abstract The temperature parameter plays an important role in the action selection based on Softmax function which is used to transform an original vector into a probability vector. An efficient method named Opti-Softmax to determine the optimal temperature parameter for Softmax function in reinforcement learning is developed in this paper. Firstly, a new evaluation function is designed to measure the effectiveness of temperature parameter by considering the information-loss of transformation and the diversity among probability vector elements. Secondly, an iterative updating rule is derived to determine the optimal temperature parameter by calculating the minimum of evaluation function. Finally, the experimental results on the synthetic data and D -armed bandit problems demonstrate the feasibility and effectiveness of Opti-Softmax method. [ABSTRACT FROM AUTHOR]

Graphical abstract General flowchart of the proposed Reference Direction and Entropy based Many-Objective Evolutionary Algorithm (RDE-MaOEA). Highlights • A novel Reference Direction and Entropy based Many-Objective Evolutionary Algorithm is proposed for solving MaOP problem. • A grid-based approach is adopted in the non-dominated ranking criterion to eliminate dominance resistant solutions. • The convergence capacity is introduced into the second level selection and balanced with diversity based on Pareto entropy. Abstract Evolutionary algorithms have been successfully applied in dealing with multi-objective optimization problems with two or three objectives. However, when solving the problem with more than 3 objectives (also called as many-objective optimizing problem), most multi-objective evolutionary algorithms perform poorly due to the ineffectiveness of Pareto dominance relationship in a high-dimensional space, and the diversity maintenance mechanism usually leads the population to be far from the true Pareto front. In this paper, a novel approach is proposed to handle the challenges in the many-objective optimization problem. Firstly, a grid-based approach is adopted to eliminate dominance resistant solutions which are non-dominated solutions with excellent diversity while incur the dominance resistance and lead the population far from the true Pareto front. Secondly, a new diversity maintenance mechanism based on reference directions is proposed, which not only enhances the diversity but also takes the convergence into consideration. For a domination- relationship based MOEA hardly has enough convergence capability for a high-dimension optimizing problem, our approach embeds convergence capability into the diversity maintenance process, and balances the convergence and diversity capability according to evolutionary states and Pareto entropy. The proposed algorithm is evaluated on a number of standard benchmark functions, i.e., DTLZ1-7 and WFG1-9 with 3-, 4-, 5-, 8-, 10-objective and compared with 5 state-of-the-art Many-Objective Evolutionary Algorithms (MaOEAs). Experimental results demonstrate the proposed algorithm’s competitiveness in both convergence and diversity in solving Many-Objective Optimization Problems. [ABSTRACT FROM AUTHOR]

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements. [ABSTRACT FROM PUBLISHER]

We investigate in this paper nonconvex binary quadratically constrained quadratic programming (QCQP) which arises in various real-life fields. We propose a novel approach of getting quadratic convex reformulation (QCR) for this class of optimization problem. Our approach employs quadratic surrogate functions and convexifies all the quadratic inequality constraints to construct QCR. The price of this approach is the introduction of an extra quadratic inequality. The “best” QCR among the proposed family, in terms that the bound of the corresponding continuous relaxation is best, can be found via solving a semidefinite programming problem. Furthermore, we prove that the bound obtained by continuous relaxation of our best QCR is as tight as Lagrangian bound of binary QCQP. Computational experiment is also conducted to illustrate the solution efficiency improvement of our best QCR when applied in off-the-shell software. [ABSTRACT FROM AUTHOR]

Based on practical observations on rule-based inference on RDF data, we study the problem of redundancy detection on RDF graphs in the presence of rules (in the form of Datalog rules) and constraints, (in the form of so-called tuple-generating dependencies), and with respect to queries (ranging from conjunctive queries up to more complex ones, particularly covering features of SPARQL, such as union, negation, or filters). To this end, we investigate the influence of several problem parameters (like restrictions on the size of the rules, the constraints, and/or the queries) on the complexity of detecting redundancy. The main result of this paper is a fine-grained complexity analysis of both graph and rule minimisation in various settings. [ABSTRACT FROM AUTHOR]

We present a survey and a comparison of a variety of algorithms that have been proposed over the years to minimize multi-label optimization problems based on the Potts model. Discrete approaches based on Markov Random Fields as well as continuous optimization approaches based on partial differential equations can be applied to the task. In contrast to the case of binary labeling, the multi-label problem is known to be NP hard and thus one can only expect near-optimal solutions. In this paper, we carry out a theoretical comparison and an experimental analysis of existing approaches with respect to accuracy, optimality and runtime, aimed at bringing out the advantages and short-comings of the respective algorithms. Systematic quantitative comparison is done on the Graz interactive image segmentation benchmark. This paper thereby generalizes a previous experimental comparison (Klodt et al. ) from the binary to the multi-label case. [ABSTRACT FROM AUTHOR]

A considerable amount of work has been done in data clustering research during the last four decades, and a myriad of methods has been proposed focusing on different data types, proximity functions, cluster representation models, and cluster presentation. However, clustering remains a challenging problem due to its ill-posed nature: it is well known that off-the-shelf clustering methods may discover different patterns in a given set of data, mainly because every clustering algorithm has its own bias resulting from the optimization of different criteria. This bias becomes even more important as in almost all real-world applications, data is inherently high-dimensional and multiple clustering solutions might be available for the same data collection. In this respect, the problems of projective clustering and clustering ensembles have been recently defined to deal with the high dimensionality and multiple clusterings issues, respectively. Nevertheless, despite such two issues can often be encountered together, existing approaches to the two problems have been developed independently of each other. In our earlier work Gullo et al. (Proceedings of the international conference on data mining (ICDM), ) we introduced a novel clustering problem, called projective clustering ensembles (PCE): given a set (ensemble) of projective clustering solutions, the goal is to derive a projective consensus clustering, i.e., a projective clustering that complies with the information on object-to-cluster and the feature-to-cluster assignments given in the ensemble. In this paper, we enhance our previous study and provide theoretical and experimental insights into the PCE problem. PCE is formalized as an optimization problem and is designed to satisfy desirable requirements on independence from the specific clustering ensemble algorithm, ability to handle hard as well as soft data clustering, and different feature weightings. Two PCE formulations are defined: a two-objective optimization problem, in which the two objective functions respectively account for the object- and feature-based representations of the solutions in the ensemble, and a single-objective optimization problem, in which the object- and feature-based representations are embedded into a single function to measure the distance error between the projective consensus clustering and the projective ensemble. The significance of the proposed methods for solving the PCE problem has been shown through an extensive experimental evaluation based on several datasets and comparatively with projective clustering and clustering ensemble baselines. [ABSTRACT FROM AUTHOR]

In this paper, we present a new Joint Source-Channel Coding (JSCC) architecture to extend the coverage of H.264/AVC High Definition (HD) video delivery over Digital Subscriber Line (DSL). The proposed solution combines low complexity H.264/AVC transrating as well as multi-carrier transmission and takes into account realistic ADSL2 specifications including all OSI layers. Both transrating and bit and power loading transmission parameters are automatically optimized in terms of end-user perceived quality, with respect to the characteristics of the given subscriber's loop. Several originalities have been included: a new optimization algorithm has been developed, as well as a full rate-distortion modelling of the H.264/AVC transrater's performances. Simulation results show that the proposed solution can extend the coverage area of HD video delivery up to more than one kilometre. It should allow the widespread distribution of HD video contents and increase the number of eligible subscribers. [ABSTRACT FROM AUTHOR]

We propose a computationally feasible way of deriving the identified features of models with multiple equilibria in pure or mixed strategies. It is shown that in the case of Shapley regular normal form games, the identified set is characterized by the inclusion of the true data distribution within the core of a Choquet capacity, which is interpreted as the generalized likelihood of the model. In turn, this inclusion is characterized by a finite set of inequalities and efficient and easily implementable combinatorial methods are described to check them. In all normal form games, the identified set is characterized in terms of the value of a submodular or convex optimization program. Efficient algorithms are then given and compared to check inclusion of a parameter in this identified set. The latter are illustrated with family bargaining games and oligopoly entry games. [ABSTRACT FROM PUBLISHER]

The execution time of many scientific computing applications is dominated by the time spent in performing sparse matrix vector multiplication (SMV; y ← A · x). We consider improving the performance of SMV on multicores by exploiting the dense substructures that are inherently present in many sparse matrices derived from partial differential equation models. First, we identify indistinguishable vertices, i.e., vertices with the same adjacency structure, in a graph representation of the sparse matrix (A) and group them into a supernode. Next, we identify effectively dense blocks within the matrix by grouping rows and columns in each supernode. Finally, by using a suitable data structure for this representation of the matrix, we reduce the number of load operations during SMV while exactly preserving the original sparsity structure of A. In addition, we use ordering techniques to enhance locality in accesses to the vector, x, to yield an SMV kernel that exploits the effectively dense substructures in the matrix. We evaluate our scheme on Intel Nehalem and AMD Shanghai processors. We observe that for larger matrices on the Intel Nehalem processor, our method improves performance on average by 37.35% compared with the traditional compressed sparse row scheme (a blocked compressed form improves performance on average by 30.27%). Benefits of our new format are similar for the AMD processor. More importantly, if we pick for each matrix the best among our method and the blocked compressed scheme, the average performance improvements increase to 40.85%. Additional results indicate that the best performing scheme varies depending on the matrix and the system. We therefore propose an effective density measure that could be used for method selection, thus adding to the variety of options for an auto-tuned optimized SMV kernel that can exploit sparse matrix properties and hardware attributes for high performance. [ABSTRACT FROM AUTHOR]

In this correspondence, a two-channel linear phase finite-impulse- response (FIR) quadrature mirror filter (QMF) bank minimax design problem is formulated as a nonconvex optimization problem so that a weighted sum of the maximum amplitude distortion of the filter bank, the maximum passband ripple magnitude and the maximum stopband ripple magnitude of the prototype filter is minimized subject to specifications on these performances. A modified filled function method is proposed for finding the global minimum of the nonconvex optimization problem. Computer numerical simulations show that our proposed design method is efficient and effective. [ABSTRACT FROM AUTHOR]

The paper presents the main characteristics of applications which are working with very large data sets and the issues related to security. First section addresses the optimization process and how it is approached when dealing with security. The second section describes the concept of very large datasets management while in the third section the risks related are identified and classified. Finally, a security optimization schema is presented with a cost-efficiency analysis upon its feasibility. Conclusions are drawn and future approaches are identified. [ABSTRACT FROM AUTHOR]

It is commonly believed that diversity is crucial for an evolutionary system to succeed, especially when the problem to be solved contains local optima from which the population cannot easily escape. There exist numerous methods to measure population diversity, but none of these have been shown to be consistently useful. In this paper, a new diversity measure is introduced, and it is shown that high diversity according to this new measure generally leads to a more successful overall evolution in most of the cases considered. [ABSTRACT FROM AUTHOR]

A case-based distance model to solve sorting problems in multiple-criteria decision analysis (MCDA) is developed, and its application in water resources management is presented. The sorting problem in MCDA is to arrange a set of alternatives into ordered groups. MCDA is introduced as consequence-based preference aggregation, whereby consequence and preference expressions (values and weights) are defined and combined in a sequence of steps. Then, sorting problems are defined, and some properties are explained. Based on weighted Euclidean distance, two case-based distance models are developed for sorting using weights and group thresholds obtained by assessment of a case set provided by a decision maker (DM). This case-based method can elicit the DM's preferences more expeditiously and accurately than direct inquiry. Case-based sorting model I is designed for cardinal criteria, while its extension, i.e., case-based sorting model II, can handle both cardinal and ordinal criteria. Optimization programs are employed to find the most descriptive weights and group thresholds. A case study in which Canadian municipalities are sorted according to water usage is presented. [ABSTRACT FROM AUTHOR]

In recent years, it has been increasingly recognized that constraint and integer programming have orthogonal and complementary strengths in stating and solving combinatorial optimization applications. In addition, their integration has become an active research topic. The optimization programming language OPL was a first attempt at integrating these technologies both at the language and at the solver levels. In particular, OPL is a modeling language integrating the rich language of constraint programming and the ability to specify search procedures at a high level of abstraction. Its implementation includes both constraint and mathematical programming solvers, as well as some cooperation schemes to make them collaborate on a given problem. The purpose of this paper is to illustrate, using OPL, the constraint-programming approach to combinatorial optimization and the complementary strengths of constraint and integer programming. [ABSTRACT FROM AUTHOR]

Angle position sensors (APSs) usually require initial calibration to improve their accuracy. This article introduces a novel offline self-calibration scheme in which a signal flow network is employed to reduce the amplitude errors, direct-current (DC) offsets, and phase shift without requiring extra calibration instruments. In this approach, a signal flow network is firstly constructed to overcome the parametric coupling caused by the linearization model and to ensure the independence of the parameters. The model parameters are stored in the nodes of the network, and the intermediate variables are input into the optimization pipeline to overcome the local optimization problem. A deep learning algorithm is also used to improve the accuracy and speed of convergence to a global optimal solution. The results of simulations show that the proposed method can achieve a high identification accuracy with a relative parameter identification error less than 0.001‰. The practical effects were also verified by implementing the developed technique in a capacitive APS, and the experimental results demonstrate that the sensor error after signal calibration could be reduced to only 6.98%. [ABSTRACT FROM AUTHOR]

This article is an extended abstract of a doctoral dissertation on chess evaluation function tuning with differential evolution (DE) algorithm. DE is adopted for efficient chess evaluation function tuning, extended with an opposition-based optimization and a new history mechanism. Experimental results show that the algorithm is efficient and can be applied to the chess evaluation function tuning that has more or less knowledge. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: We do not yet know how best to design, implement, and use health information technology (IT). A comprehensive framework that captures knowledge on the implementation, use, and optimization of health IT will help guide more effective approaches in the future. Methods: The authors conducted a targeted review of existing literature on health IT implementation and use, including health IT-related theories and models. By crosswalking elements of current theories and models, the authors identified five major facets of an organizational framework that provides a structure to organize and capture information on the implementation and use of health IT. Results: The authors propose a novel organizational framework for health IT implementation and use with five major facets: technology, use, environment, outcomes, and temporality. Each major facet is described in detail along with associated categories and measures. Conclusion: The proposed framework is an essential first step toward ensuring a more consistent and comprehensive understanding of health IT implementation and use and a more rigorous approach to data collection, measurement development, and theory building. [Copyright &y& Elsevier]

Constraint programming (CP) is a young but rapidly developing technology that supports the modeling and solution of a wide range of planning, scheduling, search, and optimization problems. The integration of CP concepts into languages from other paradigms yields constraint-based multiparadigm programming. This article briefly introduces the constraint-based paradigm and discusses methods, problems, gains, and experiences in integrating CP techniques into the object-oriented paradigm, using Java as an example. [ABSTRACT FROM PUBLISHER]

The article presents a perspective on the ISI Impact Factor of the journal "Journal of Computing." It informs that journal has achieved an over 1.5 which ranks third among the 56 "OR/MS" journals, second among the 6 INFORMS journals. It announces that the former "Logic, Optimization, and Constraint Programming" area of the journal has been altered by area editor John N. Hooker Jr.