An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usually solved with local solvers. Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality constraints; however, a drawback was tha...

25 CRITICAL EVENT TABU SEARCH FOR MULTIDIMENSIONAL KNAPSACK PROBLEMS Fred Glover Graduate School of Business, Box 419 University of Colorado at Boulder Boulder, Colorado, 80309-0419 E-Mail: [email protected] Gary A. Kochenberger College of Business University of Colorado at Denver Denver, Colorado 80217-3364 E-Mail: [email protected] We report a new approach to creating a ...

In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The...

We address the issue of fitting a surrogate to data generated from numerical simulators that have tunable fidelity. In particular, we focus on problems where data is generated using Monte-Carlo simulations. We show that this problem is similar to optimal design of experiments (DOE) but includes additional variables: the accuracy of the response at each point, and a computational time constraint...

Currently surrogate data analysis can be used to determine if data is consistent with various linear systems, or something else (a nonlinear system). In this paper we propose an extension of these methods in an attempt to make more specific classifications within the class of nonlinear systems. In the method of surrogate data one estimates the probability distribution of values of a test statis...

Three different strategies are identified for organizational adaptation, including dynamic process selection. An executable organizational model composed of individual models of a five stage interacting decision maker is used to evaluate the effectiveness of the different adaptation strategies on organizational performance. The concept of entropy is used to calculate the total activity value, a...

We use statistical machine learning to develop methods for automatically designing mechanisms in domains without money. Our goal is to find a mechanism that best approximates a given target function subject to a design constraint such as strategy-proofness or stability. The proposed approach involves identifying a rich parametrized class of mechanisms that resemble discriminantbased multiclass ...

Satisfiability problems (SAT) are capable of representing many important realworld problems, like planning, scheduling, and robotic movement. Efficient encodings exist for many of these applications and thus having good solvers for these problems is of critical significance. We look at how adaptive memory and surrogate constraint processes can be used as search guidance for both constructive an...

BACKGROUND Surrogate endpoint trials test strategies more efficiently but are accompanied by uncertainty about the relationship between changes in surrogate markers and clinical outcomes. METHODS AND RESULTS We identified cardiovascular trials with primary surrogate endpoints published in the New England Journal of Medicine, Lancet, and JAMA: Journal of the American Medical Association from 1...