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pro vyhledávání: '"Burke, James V."'
Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult than for line
Externí odkaz:
http://arxiv.org/abs/2205.06925
Autor:
Burke, James V., Lin, Qiuying
The convergence theory for the gradient sampling algorithm is extended to directionally Lipschitz functions. Although directionally Lipschitz functions are not necessarily locally Lipschitz, they are almost everywhere differentiable and well approxim
Externí odkaz:
http://arxiv.org/abs/2107.04918
Compressed sensing is a central topic in signal processing with myriad applications, where the goal is to recover a signal from as few observations as possible. Iterative re-weighting is one of the fundamental tools to achieve this goal. This paper r
Externí odkaz:
http://arxiv.org/abs/1910.07095
In this note we provide a full conjugacy and subdifferential calculus for convex convex-composite functions in finite-dimensional space. Our approach, based on infimal convolution and cone-convexity, is straightforward and yields the desired results
Externí odkaz:
http://arxiv.org/abs/1907.08318
Tracking underwater autonomous platforms is often difficult because of noisy, biased, and discretized input data. Classic filters and smoothers based on standard assumptions of Gaussian white noise break down when presented with any of these challeng
Externí odkaz:
http://arxiv.org/abs/1905.09373
We show that many important convex matrix functions can be represented as the partial infimal projection of the generalized matrix fractional (GMF) and a relatively simple convex function. This representation provides conditions under which such func
Externí odkaz:
http://arxiv.org/abs/1807.01187
Autor:
Burke, James V., Engle, Abraham
We consider descent methods for solving non-finite valued nonsmooth convex-composite optimization problems that employ Gauss-Newton subproblems to determine the iteration update. Specifically, we establish the global convergence properties for descen
Externí odkaz:
http://arxiv.org/abs/1806.05218
Autor:
Burke, James V., Engle, Abraham
This work concerns the local convergence theory of Newton and quasi-Newton methods for convex-composite optimization: minimize f(x):=h(c(x)), where h is an infinite-valued proper convex function and c is C^2-smooth. We focus on the case where h is in
Externí odkaz:
http://arxiv.org/abs/1805.01073
Autor:
Burke, James V., Curtis, Frank E., Lewis, Adrian S., Overton, Michael L., Simões, Lucas E. A.
This paper reviews the gradient sampling methodology for solving nonsmooth, nonconvex optimization problems. An intuitively straightforward gradient sampling algorithm is stated and its convergence properties are summarized. Throughout this discussio
Externí odkaz:
http://arxiv.org/abs/1804.11003
This paper focuses on the design of sequential quadratic optimization (commonly known as SQP) methods for solving large-scale nonlinear optimization problems. The most computationally demanding aspect of such an approach is the computation of the sea
Externí odkaz:
http://arxiv.org/abs/1803.09224