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We consider the problem faced by a company that wants to use viral marketing to introduce a new product into a market where a competing product is already being introduced. We assume that consumers will use only one of the two products and will influence their friends in their decision of which product to use. We propose two models for the spread of… (More)

We propose data profiles as a tool for analyzing the performance of derivative-free optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and… (More)

- M. Kortelainen, J. McDonnell, W. Nazarewicz, P.-G. Reinhard, J. Sarich, N. Schunck +2 others
- 2011

A new Skyrme-like energy density suitable for studies of strongly elongated nuclei has been determined in the framework of the Hartree-Fock-Bogoliubov theory using the recently developed model-based, derivative-free optimization algorithm pounders. A sensitivity analysis at the optimal solution has revealed the importance of states at large deformations in… (More)

We present a new derivative-free algorithm, ORBIT, for unconstrained local optimization of computationally expensive functions. A trust-region framework using interpolating Radial Basis Function (RBF) models is employed. The RBF models considered often allow ORBIT to interpolate nonlinear functions using fewer function evaluations than the polynomial models… (More)

- Stefan M. Wild
- 2008

We introduce MNH, a new algorithm for unconstrained optimization when derivatives are unavailable, primarily targeting applications that require running computationally expensive deterministic simulations. MNH relies on a trust-region framework with an underdetermined quadratic model that interpolates the function at a set of data points. We show how to… (More)

- Robert B. Gramacy, Genetha A. Gray, Sébastien Le Digabel, Herbert K.H. Lee, Pritam Ranjan, Garth Wells +1 other
- 2014

Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literature is sparse, especially in addressing problems with nontrivial constraints. This situation is unfortunate because statistical methods have many attractive properties: global scope, handling noisy… (More)

We analyze globally convergent, derivative-free trust-region algorithms relying on radial basis function interpolation models. Our results extend the recent work of Conn, Scheinberg, and Vicente [SIAM J. Optim., 20 (2009), pp. 387–415] to fully linear models that have a nonlinear term. We characterize the types of radial basis functions that fit in our… (More)

Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Thesis directed by Professor James Curry The size of data sets found in practice often prevents standard information retrieval and machine learning… (More)