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In topic modeling, identifiability of the topics is an essential issue. Many topic modeling approaches have been developed under the premise that each topic has a characteristic anchor word that only appears in that topic. The anchor-word assumption is fragile in practice, because words and terms have multiple uses; yet it is commonly adopted because it enables identifiability guarantees. Remedies in the literature include using three- or higher-order word co-occurence statistics to come up with tensor factorization models, but such statistics need many more samples to obtain reliable estimates, and identifiability still hinges on additional assumptions, such as consecutive words being persistently drawn from the same topic. In this work, we propose a new topic identification criterion using second order statistics of the words. The criterion is theoretically guaranteed to identify the underlying topics even when the anchor-word assumption is grossly violated. An algorithm based on alternating optimization, and an efficient primal-dual algorithm are proposed to handle the resulting identification problem. The former exhibits high performance and is completely parameter-free; the latter affords up to 200 times speedup relative to the former, but requires step-size tuning and a slight sacrifice in accuracy. A variety of real text copora are employed to showcase the effectiveness of the approach, where the proposed anchor-free method demonstrates substantial improvements compared to a number of anchor-word based approaches under various evaluation metrics. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is widely used for subsurface detection due to the nondestructive characteristics. GPR signal is non-stationary because of complex medium environment, and time-frequency analysis is the powerful tool for the research of GPR signal. In this paper, a new generalized S transform with parameters optimization is proposed to analyze the GPR signal. In the proposed scheme, first a flexible window function replaces the fixed window function of S transform, then the criterion of time-frequency concentration is used to optimize the parameters of the window function, the aim is to improve the time-frequency resolution and applicability of S transform. The experimental results for synthetic data and practical GPR data show the proposed scheme can enhance the energy concentration in time-frequency domain effectively and provide better layer recognition and target detection performance. [ABSTRACT FROM AUTHOR]

Multidimensional scaling (MDS) refers to a class of dimensionality reduction techniques, which represent entities as points in a low-dimensional space so that the interpoint distances approximate the initial pairwise dissimilarities between entities as closely as possible. The traditional methods for solving MDS are susceptible to outliers. Here, a unified framework is proposed, where the MDS is treated as maximization of a correntropy criterion, which is solved by half-quadratic optimization in either multiplicative or additive forms. By doing so, MDS can cope with an initial dissimilarity matrix contaminated with outliers because the correntropy criterion is closely related to $M$-estimators. Three novel algorithms are derived. Their performances are assessed experimentally against three state-of-the-art MDS techniques, namely the scaling by majorizing a complicated function, the robust Euclidean embedding, and the robust MDS under the same conditions. The experimental results indicate that the proposed algorithms perform substantially better than the aforementioned competing techniques. [ABSTRACT FROM PUBLISHER]

In this paper, the collaborative optimization is improved to tackle multidisciplinary problems with coupling design variables within disciplines. The design variables are first distinctly classified into private and public ones according to disciplines. A criterion is then proposed to judge the coupling of private variables with the public variables. For the coupling case, a strategy is suggested to decouple the variables before performing the collaborative optimization. To demonstrate the effectiveness of the improved collaborative optimization, two practical optimization problems are comparatively studied for uncoupled design variables as well as coupled ones. It is shown that the present framework can achieve the better optimal results as compared with other methods. Furthermore, the judge criterion and the decoupling strategy are also validated. [ABSTRACT FROM AUTHOR]

Abstract: In this work an optimization problem on a classical elementary stochastic system, modeled as an Erlang-B (M/M/ ) loss system, is formulated by using a bicriteria approach. The problem is focused on the allocation of a given total of servers to a number of groups of servers capable of carrying certain offered traffic processes assumed as Poissonian in nature. Two main objectives are present in this formulation. First a criterion of equity in the grade of service, measured by the call blocking probabilities, entails that the absolute difference between the blocking probabilities experienced by the calls in the different service groups must be as small as possible. Second a criterion of system economic performance optimization requires the total traffic carried by the system, to be maximized. Relevant mathematical results characterizing the two objective functions and the set of the non-dominated solutions, are presented. An algorithm for traveling on based on the resolution of single criterion convex problems, using a Newton–Raphson method, is also proposed. In each iteration the two first derivatives of the Erlang-B function in the number of circuits (a difficult numerical problem) are calculated using a method proposed earlier. Some computational results are also presented. [Copyright &y& Elsevier]

This paper aims at studying the generalized well-posedness in the sense of Bednarczuk for set optimization problems with set-valued maps. Three kinds of B-well-posedness for set optimization problems are introduced. Some relations among the three kinds of B-well-posedness are established. Necessary and sufficient conditions of well-posedness for set optimization problems are obtained. [ABSTRACT FROM AUTHOR]

Abstract: A new scheduling model with two-agent and sum-of-processing-times-based deterioration is proposed in this paper. Two agents compete to perform their respective jobs on a common single machine and each agent has his own criterion to optimize. The sum-of-processing-times-based deterioration of a job is that the actual job processing time is a function of the sum of the normal processing times of the already processed jobs. The objective is to minimize the total completion time of the first agent with the restriction that the makespan of the second agent cannot exceed a given upper bound. The optimal properties of the problems are given and the optimal polynomial time algorithms are proposed to solve the scheduling problems. [Copyright &y& Elsevier]

Abstract: In the past, research on multiple criteria scheduling assumes that the number of available machines is fixed during the whole scheduling horizon and research on scheduling with limited machine availability assumes that there is only a single criterion that needs to be optimized. Both assumptions may not hold in real life. In this paper we simultaneously consider both bicriteria scheduling and scheduling with limited machine availability. We focus on two parallel machines’ environment and the goal is to find preemptive schedules to optimize both makespan and total completion time subject to machine availability. Our main contribution in this paper is that we showed three bicriteria scheduling problems are in P by providing polynomial time optimal algorithms. [Copyright &y& Elsevier]

A new cut selection criterion for Benders’ cuts is proposed and computationally analyzed. The results show that the new criterion is more robust—and often considerably faster—than the standard ones. [ABSTRACT FROM AUTHOR]

This paper presents two new methods to optimize the selection of minimum number of thermal sensors for machine tool thermal error compensation. The two methods, namely, direct criterion method and indirect grouping method, are based on the synthetic grey correlation theory. They are applied to analyze the data of an air cutting experiment on a CNC turning center. After optimization, the number of thermal points reduced from 16 to four. Thus, for machine tool thermal error modeling, the number of temperature variables is greatly reduced while coupling problems among temperature variables can be avoided. A real cutting experiment is then conducted to verify the efficiency of the presented optimization methods under practical manufacturing conditions. The comparison of the results between the model with all 16 thermal sensors and the model with four optimized thermal sensors indicates that, after optimization, the model accuracy is greatly improved. [ABSTRACT FROM AUTHOR]

Abstract  Consider the problem of discriminating between two rival response surface models and estimating parameters in the identified model. To construct designs serving for both model discrimination and parameter estimation, the M γ-optimality criterion, which puts weight γ (0≤γ≤1) for model discrimination and 1 − γ for parameter estimation, is adopted. The corresponding M γ-optimal product design is explicitly derived in terms of canonical moments. With the application of the maximin principle on the M γ-efficiency of any M γ'-optimal product design, a criterion-robust optimal product design is proposed. [ABSTRACT FROM AUTHOR]

A large class of clustering problems can be formulated as an optimizational problem in which the best clustering is searched for among all feasible clustering according to a selected criterion function. This clustering approach can be applied to a variety of very interesting clustering problems, as it is possible to adapt it to a concrete clustering problem by an appropriate specification of the criterion function and/or by the definition of the set of feasible clusterings. Both, the blockmodeling problem (clustering of the relational data) and the clustering with relational constraint problem (clustering of the attribute and relational data) can be very successfully treated by this approach. It also opens many new developments in these areas. The paired clustering approaches are applied to the Slovenian scientific collaboration data. [ABSTRACT FROM AUTHOR]

Abstract: With the integral approach of global optimization, new optimality conditions for global minimization are proposed and studied. Using m-mean value condition one can design algorithms for finding global minimizers. With ν-variance condition, one can set the stopping criterion. [Copyright &y& Elsevier]