Author
Saman Babaie-Kafaki
Other affiliations: Sharif University of Technology
Bio: Saman Babaie-Kafaki is an academic researcher from Semnan University. The author has contributed to research in topics: Conjugate gradient method & Nonlinear conjugate gradient method. The author has an hindex of 16, co-authored 66 publications receiving 696 citations. Previous affiliations of Saman Babaie-Kafaki include Sharif University of Technology.
Papers
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TL;DR: It is shown that the methods of the suggested class of Dai–Liao conjugate gradient methods are globally convergent for uniformly convex objective functions.
Abstract: Based on an eigenvalue study, a descent class of Dai–Liao conjugate gradient methods is proposed. An interesting feature of the proposed class is its inclusion of the efficient nonlinear conjugate gradient methods proposed by Hager and Zhang, and Dai and Kou, as special cases. It is shown that the methods of the suggested class are globally convergent for uniformly convex objective functions. Numerical results are reported, they demonstrate the efficiency of the proposed methods in the sense of the performance profile introduced by Dolan and More.
81 citations
TL;DR: Two modified conjugate gradient methods are proposed by Dai and Liao and it is briefly shown that the methods are globally convergent when the line search fulfills the strong Wolfe conditions.
80 citations
TL;DR: Two nonlinear conjugate gradient methods for unconstrained optimization problems are introduced based on a modified version of the secant equation proposed by Zhang, Deng and Chen, and Zhang and Xu and the modified BFGS update proposed by Yuan.
75 citations
TL;DR: In this article, two adaptive choices for the parameter of Dai-Liao conjugate gradient (CG) method are suggested, one of which is obtained by minimizing the distance between search directions of Dai -Liao method and a three-term CG method proposed by Zhang et al.
Abstract: Two adaptive choices for the parameter of Dai–Liao conjugate gradient (CG) method are suggested. One of which is obtained by minimizing the distance between search directions of Dai–Liao method and a three-term CG method proposed by Zhang et al. and the other one is obtained by minimizing Frobenius condition number of the search direction matrix. Global convergence analyses are made briefly. Numerical results are reported; they demonstrate effectiveness of the suggested adaptive choices.
33 citations
TL;DR: Based on an eigenvalue analysis, a descent class of two-parameter extension of the conjugate gradient method proposed by Polak and Ribiere (1969), and Polyak (1969) is suggested.
Abstract: Based on an eigenvalue analysis, a descent class of two-parameter extension of the conjugate gradient method proposed by Polak and Ribiere (1969), and Polyak (1969) is suggested. It is interesting that the one-parameter class of descent conjugate gradient methods proposed by Yu et?al. (2008) is a member of the suggested class. Global convergence analysis for the methods of the suggested class is made briefly. Preliminary numerical results are reported; they demonstrate proper choices for the parameters of the suggested class of the conjugate gradient methods that may lead to a promising computational performance.
32 citations
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01 Jan 1988
TL;DR: The mathematical formulation of the simulated annealing algorithm is extended to continuous optimization problems, and it is proved asymptotic convergence to the set of global optima.
Abstract: In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of Rn in which some real valued functionf assumes its optimal (i.e. maximal or minimal) value. We present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing algorithm as originally given for discrete optimization problems. The mathematic formulation is extended to continuous optimization problems and we prove asymptotic convergence to the set of global optima. Furthermore, we discuss an implementation of the algorithm and compare its performance with other well known algorithms. The performance evaluation is carried out for a standard set of test functions from the literature. Keywords: global optimization, continuous variables, simulated annealing.
382 citations
01 Jan 2007
TL;DR: It is concluded that the proposed approach can assist decision makers in selecting suitable R&D portfolios, while there is a lack of reliable project information.
Abstract: Making R&D portfolio decision is difficult, because long lead times of R&D and market and technology dynamics lead to unavailable and unreliable collected data for portfolio management. The objective of this research is to develop a fuzzy R&D portfolio selection model to hedge against the R&D uncertainty. Fuzzy set theory is applied to model uncertain and flexible project information. Since traditional project valuation methods often underestimate the risky project, a fuzzy compound-options model is used to evaluate the value of each R&D project. The R&D portfolio selection problem is formulated as a fuzzy zero-one integer programming model that can handle both uncertain and flexible parameters to determine the optimal project portfolio. A new transformation method based on qualitative possibility theory is developed to convert the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse perspective. The transformed model can be solved by an optimization technique. An example is used to illustrate the proposed approach. We conclude that the proposed approach can assist decision makers in selecting suitable R&D portfolios, while there is a lack of reliable project information.
266 citations
01 Mar 2013
TL;DR: A robust and sparse estimator is introduced by adding an L1penalty on the coefficient estimates to the well-known least trimmed squares (LTS) estimator and it is shown that the sparse LTS has better prediction performance than its competitors in the presence of leverage points.
Abstract: Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data. This paper combines robust regression and sparse model estimation. A robust and sparse estimator is introduced by adding an L1penalty on the coefficient estimates to the well-known least trimmed squares (LTS) estimator. The breakdown point of this sparse LTS estimator is derived, and a fast algorithm for its computation is proposed. In addition, the sparse LTS is applied to protein and gene expression data of the NCI-60 cancer cell panel. Both a simulation study and the real data application show that the sparse LTS has better prediction performance than its competitors in the presence of leverage points.
150 citations
TL;DR: This study describes seven principles for locating earthquake evacuation shelters and proposes a multi-criteria constraint location model that can be used to solve the location problem and concludes that the evacuation shelter location model and solution method are effective and suitable to solved the multi-Criteria shelter location problem from an urban planning perspective.
Abstract: Earthquakes are serious natural disasters that can result in significant fatalities and economic loss The building of earthquake evacuation shelters is an effective way to reduce earthquake disaster risk and protect lives Current studies on facility location models commonly overlook multiple optimal criteria from an urban planning perspective and are not suited to planning earthquake evacuation shelters In this study, we describe seven principles for locating earthquake evacuation shelters Following these principles, we propose a multi-criteria constraint location model that can be used to solve the location problem We then present an iterative method to solve the model With the support of a geographic information system (GIS), the method is composed of three steps: select candidate shelters, analyze the spatial coverage of candidate shelters and determine the shelter locations Finally, a case study is used to demonstrate the application of the multi-criteria model and the corresponding solution method for its effectiveness in planning urban earthquake evacuation shelters We conclude that the evacuation shelter location model and solution method are effective and suitable to solve the multi-criteria shelter location problem from an urban planning perspective
82 citations
TL;DR: It is shown that the methods of the suggested class of Dai–Liao conjugate gradient methods are globally convergent for uniformly convex objective functions.
Abstract: Based on an eigenvalue study, a descent class of Dai–Liao conjugate gradient methods is proposed. An interesting feature of the proposed class is its inclusion of the efficient nonlinear conjugate gradient methods proposed by Hager and Zhang, and Dai and Kou, as special cases. It is shown that the methods of the suggested class are globally convergent for uniformly convex objective functions. Numerical results are reported, they demonstrate the efficiency of the proposed methods in the sense of the performance profile introduced by Dolan and More.
81 citations