We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as contextual bandits, encompasses a wide variety of applications including health-care policy and Internet advertising. A central task is evaluation of a new policy given historic data consisting of contexts, actions an...

In this paper, we propose a new methodology for handling optimization problems with uncertain data. With the usual Robust Optimization paradigm, one looks for the decisions ensuring a required performance for all realizations of the data from a given bounded uncertainty set, whereas with the proposed approach, we require also a controlled deterioration in performance when the data is outside th...

In this paper, we propose a new methodology for handling optimization problems with uncertain data. With the usual Robust Optimization paradigm, one looks for the decisions ensuring a required performance for all realizations of the data from a given bounded uncertainty set, whereas with the proposed approach, we require also a controlled deterioration in performance when the data is outside th...

The optimization of network protocol parameters based on network simulation can be considered a “black-box” optimization problem with unknown, multi-modal and noisy objective functions. In this paper, an adaptive random search algorithm is proposed to perform efficient and robust optimization for the concerned problems. Specifically, the algorithm is designed for use by the on-line simulation s...

We consider the design of tapers for coupling power between uniform and slowlight periodic waveguides. We describe new optimization methods for designing robust tapers, which not only perform well under nominal conditions, but also over a given set of parameter variations. When the set of parameter variations models the inevitable variations typical in the manufacture or operation of the couple...

We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization. In the main part of the paper we show that if U i...

In this paper, we consider an adaptive approach to address optimization problems with uncertain cost parameters. Here, the decision maker selects an initial decision, observes the realization of the uncertain cost parameters, and then is permitted to modify the initial decision. We treat the uncertainty using the framework of robust optimization in which uncertain parameters lie within a given ...

a soil bacterium capable of degrading chitin on chitin agar plates was isolated and identified as bacillus pumilus isolate u5 on the basis of 16s rdna sequence analysis. in order to optimize culture conditions for chitinase production by this bacterium, a two step approach was employed. first, the effects of several medium components were studied using the plackett-burman design. among various ...