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First-order methods for large scale optimisation problems - Additional large-scale algorithms

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If you have a question about this talk, please contact Rachel Furner.

Part of the CCIMI short course ‘First-order methods for large scale optimisation problems’

Instructor: Stephen Becker, University of Colorado

Lecture 4: Additional large-scale algorithms A variety of useful methods are discussed, with comments but without detailed analysis. Algorithms may include: the simplex method for LPs, classical algorithms for unconstrained problems (non-linear conjugate gradient, quasi-Newton, matrix-free Newton, Levenberg–Marquardt and Gauss-Newton, and active-set approaches), classical algorithms for constrained problems (penalty methods, Augmented Lagrangian, ADMM and Douglas-Rachford, coordinate descent and alternating minimization, interior-point methods, sequential quadratic programming, Frank-Wolfe and conditional gradient), and possibly primal-dual methods and mirror-descent. There will be an interlude on non-convex optimization and some non-convex algorithms (e.g., cubic-regularization).

This talk is part of the CCIMI Short course: First-order methods for large scale optimisation problems series.

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