Abstract
We present a framework for the development of globally defined descent algorithms for the minimization of non-differentiable objective functionsF := h º f withh convex. Within our structure the global convergence properties of the Cauchy, Modified Newton, Gauss—Newton, and Variable-Metric methods are easily established along with that of several new approaches. Examples illustrating the calculational techniques are provided.
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Burke, J.V. Descent methods for composite nondifferentiable optimization problems. Mathematical Programming 33, 260–279 (1985). https://doi.org/10.1007/BF01584377
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DOI: https://doi.org/10.1007/BF01584377