Approaches toward (eta(2)-HX) (X = H, SiR (R = Me-3 or Me2Ph), CH3) sigma-complexes of ruthenium

Two new Ru(II)-complexes RuH(Tpms)(PPh3)(2)] 1 (Tpms - (C3H3N2)(3)CSO3, tris-(pyrazolyl) methane sulfonate) and Ru(OTf)(Tpms)(PPh3)(2)] 2 (OTf = CF3SO3) have been synthesized and characterized wherein Ru-H and Ru-OTf are the key reactive centers. Reaction of 1 with HOTf results in the Ru(eta(2)-H-2)(Tpms)(PPh3)(2)]OTf] complex 3, whereas reaction of 1 with Me3SiOTf affords the dihydrogen complex 3 and complex 1 through an unobserved sigma-silane intermediate. In addition, an attempt to characterize the sigma methane complex via reaction of complex 1 with CH3OTf yields complex 2 and free methane. On the other hand, reaction of Ru(OTf)(Tpms)(PPh3)(2)] 2 with H-2 and PhMe2SiH at low temperature resulted in sigma-H-2, 3 and a probable sigma-silane complexes, respectively. However, no sigma-methane complex was observed for the reaction of complex 2 with methane even at low temperature. (C) 2014 Elsevier B. V. All rights reserved.

Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms

We present the first q-Gaussian smoothed functional (SF) estimator of the Hessian and the first Newton-based stochastic optimization algorithm that estimates both the Hessian and the gradient of the objective function using q-Gaussian perturbations. Our algorithm requires only two system simulations (regardless of the parameter dimension) and estimates both the gradient and the Hessian at each update epoch using these. We also present a proof of convergence of the proposed algorithm. In a related recent work (Ghoshdastidar, Dukkipati, & Bhatnagar, 2014), we presented gradient SF algorithms based on the q-Gaussian perturbations. Our work extends prior work on SF algorithms by generalizing the class of perturbation distributions as most distributions reported in the literature for which SF algorithms are known to work turn out to be special cases of the q-Gaussian distribution. Besides studying the convergence properties of our algorithm analytically, we also show the results of numerical simulations on a model of a queuing network, that illustrate the significance of the proposed method. In particular, we observe that our algorithm performs better in most cases, over a wide range of q-values, in comparison to Newton SF algorithms with the Gaussian and Cauchy perturbations, as well as the gradient q-Gaussian SF algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

Simultaneous Perturbation Newton Algorithms for Simulation Optimization

We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman-Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work.