Objectives: To introduce a new two-parameter lifetime distribution that will be more flexible in modeling real data over the existing common distributions. Methods: The is generated by using Alpha Power Transformed model developed Mahdavi and Kundu. In this method, probability density function cumulative of Aradhana are used as base for generating Distribution. substituted to get real-life data...

We motivate this study from a recent work on a stochastic gradient descent (SGD) method with only one projection (Mahdavi et al., 2012), which aims at alleviating the computational bottleneck of the standard SGD method in performing the projection at each iteration, and enjoys an O(log T/T ) convergence rate for strongly convex optimization. In this paper, we make further contributions along th...

the 1rs chromosome segment derived from petkus rye carries genes for resistance to three wheat rust diseases, namely lr26 for resistance to leaf rust (caused by puccinia triticina), yr9 for resistance to stripe rust (p.striiformis f. sp. tritici) and sr31 for resistance to stem rust (p. graminis f. sp. tritici). since sec-1 is tightly linked with the three rust resis-tance genes electrophoresis...

Glaucoma is the second leading cause of blindness in the general population. The definition of end-stage glaucoma may be based on a very constricted visual field, or a markedly severed visual acuity (Gillies& Brooks et al., 2000). Many factors have been postulated to put the patient at a high risk. Achieving an individually fashioned target IOP is supposed to minimize the risk of glaucoma progr...

In the real word, there are many problems which have linear programming models and sometimes it is necessary to formulate these models with parameters of uncertainty. Many numbers from these problems are linear programming problems with fuzzy variables. Some authors considered these problems and have developed various methods for solving these problems. Recently, Mahdavi-Amiri and Nasseri (2007...

We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints, which are constraints that need to be satisfied when accumulated over a finite number of rounds T , but can be violated in intermediate rounds. For some user-defined trade-off parameter β ∈ (0, 1), the proposed algorithm achieves cumulative regret bounds of O(Tmax{β,...

We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of N...