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Iterative methods are proposed for certain augmented systems of linear equations that arise in interior methods for general nonlinear optimization. Interior methods define a sequence of KKT equations that represent the symmetrized (but indefinite) equations associated with New-ton's method for a point satisfying the perturbed optimality conditions. These(More)
We consider the problem of finding an approximate minimizer of a general quadratic function subject to a two-norm constraint. The Steihaug-Toint method minimizes the quadratic over a sequence of expanding subspaces until the iterates either converge to an interior point or cross the constraint boundary. The benefit of this approach is that an approximate(More)
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—Measurements of the magnetoquasistatic fields generated from a magnetic dipole (an electrically small current loop) located above the earth are presented and compared with calculations using complex image theory. With a horizontal (i.e., the surface normal parallel to the earth) emitting loop located at a height of h and a copolarized horizontal receiving(More)
Seventy-one children in three groups (reading disabilities, ADHD without reading disabilities, and normal controls) were compared on their ability to rapidly name colors, letters, numbers, and objects (RAN Tasks) and alternating letters/numbers and letters/numbers/colors (RAS tasks). Children with reading disabilities were found to be slower on letter- and(More)
We describe an asynchronous parallel derivative-free algorithm for linearly constrained optimization. Generating set search (GSS) is the basis of our method. At each iteration , a GSS algorithm computes a set of search directions and corresponding trial points and then evaluates the objective function value at each trial point. Asynchronous versions of the(More)
Optimization for complex systems in engineering often involves the use of expensive computer simulation. By combining statistical emulation using treed Gaussian processes with pattern search optimization, we are able to perform robust local optimization more efficiently and effectively than using either method alone. Our approach is based on the(More)