In this paper, we consider a variant of adaptive robust combinatorial optimization problems where the decision maker can prepare K solutions and choose best among them upon knowledge true data realizations. We suppose that uncertainty may affect objective constraints through functions are not necessarily linear. propose new exact algorithm for solving these when feasible set nominal problem doe...

We present a closed form expression for |Vub|/|VtbV ∗ ts| in terms of the endpoint photon and lepton spectra from the inclusive decays B → Xs γ and B → Xu l ν, respectively, which includes the resummation of the endpoint logs at next to leading order and is completely independent of the B meson structure function. The use of this expression for extracting Vub would eliminate the large systemati...

We consider a general adversarial stochastic optimization model. Our model involves the design of a system that an adversary may subsequently attempt to destroy or degrade. We introduce SPAR, which utilizes mixed-integer programming for the design decision and a Markov decision process (MDP) for the modeling of our adversarial phase. © 2005 Elsevier B.V. All rights reserved.

Long design cycles due to the inability to predict silicon realities is a well-known problem that plagues analog/RF integrated circuit product development. As this problem worsens for technologies below 100nm, the high cost of design and multiple manufacturing spins causes fewer products to have the volume required to support full custom implementation. Design reuse and analog synthesis make an...

We consider the problem of bounding the expected value of a linear program (LP) containing random coe&cients, with applications to solving two-stage stochastic programs. An upper bound for minimizations is derived from a restriction of an equivalent, penalty-based formulation of the primal stochastic LP, and a lower bound is obtained from a restriction of a reformulation of the dual. Our “restr...