We study statistical inference and robust solution methods for stochastic optimization prob-lems. We first develop an empirical likelihood framework for stochastic optimization. We showan empirical likelihood theory for Hadamard differentiable functionals with general f -divergencesand give conditions under which T (P ) = infx∈X EP [`(x; ξ)] is Hadamard differentiable. Noting<lb...

This paper discusses experimental robot identification based on a statistical framework. It presents a new approach toward the design of optimal robot excitation trajectories, and formulates the maximum-likelihood estimation of dynamic robot model parameters. The differences between the new design approach and the existing approaches lie in the parameterization of the excitation trajectory and ...

This paper presents numerical experiments solving complex robust portfolio optimization problems. The models we study are motivated by realistic considerations, and are in principle combinatorially difficult; however we show that using modern optimization methodology one can solve large, real-life cases quite efficiently. We consider classical mean-variance problems [M52], [M59] and closely rel...

Robust optimization is an important technique to immunize optimization problems against data uncertainty. In the case of a linear program and an ellipsoidal uncertainty set, the robust counterpart turns into a second-order cone program. In this work, we investigate the efficiency of linearizing the second-order cone constraints of the latter. This is done using the optimal linear outer-approxim...

The optimization of investment portfolios is the most important topic in financial decision making, and many relevant models can be found in the literature.  According to importance of portfolio optimization in this paper, deals with novel solution approaches to solve new developed portfolio optimization model. Contrary to previous work, the uncertainty of future retur...

in this paper, we introduce a model  to optimization of milk run system that is one of vrp problem with time window and uncertainty in inventory. this approach led to the routes with minimum cost of transportation while satisfying all inventory in a given bounded set of uncertainty .the problem is formulated as a robust optimization problem. since the resulted problem illustrates that grows up ...

Intense competition in the current business environment leads firms to focus on selecting the most appropriate R&D project portfolio in order to accomplish sustainable growth in the fierce market place. Achieving this goal is tied down by uncertainty which is inherent in all R&D projects. Therefore, investment decisions must be made within an optimization framework, based on the data which is u...

optimization of reservoir parameters is an important issue in petroleum exploration and production. the ant colony optimization(aco) is a recent approach to solve discrete and continuous optimization problems. in this paper, the ant colony optimization is usedas an intelligent tool to estimate reservoir rock properties. the methodology is illustrated by using a case study on shear wave velocity...

Maximum likelihood estimation (MLE) is one of the most popular technique in econometric and other statistical applications due to its strong theoretical appeal, but can lead to numerical issues when the underlying optimization problem is solved. We examine in this paper a range of trust region and line search algorithms and focus on the impact that the approximation of the Hessian matrix has on...