A cutting plane algorithm for convex programming that uses analytic centers

By David S. Atkinson, P. M. Vaidya. 1995

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Learning Optimized Risk Scores (2016, methodology): “ch is to solve RiskSlimMINLP with a cutting plane algorithm that adds cuts at central points of RiskSlimMIP Hk(;) (e.g., the center of gravity method of Levin 1965 or the analytic center methods of Atkinson and Vaidya 1995; Gon and Vial 2002), as these methods can guarantee convergence in a xed numb”
Predicting abnormal returns from news using text classification (2008, methodology): “scheme as in Atkinson & Vaidya (1995) which keeps the complexity of the localization set bounded.”
A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization (2015, unknown): “6 2.3 Submodular Function Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8”
Optimization under uncertainty with applications to design of truss structures (2008, methodology): “(1) When is convex and f (x, δ) is concave in δ, problem (1) is a saddle-point optimization problem that can be solved, for instance, by means of cutting-plane techniques (see, e.g., Atkinson and Vaidya 1995; Goffin and Vial 2002).”
Network optimization : continuous and discrete models (1998, methodology): “More recent proposals, some of which relate to interior point methods, are discussed in Goffin and Vial [1990], Goffin, Haurie, and Vial [1992], Ye [1992], Kortanek and No [1993], Goffin, Luo, and Ye [1993], [1996], Atkinson and Vaidya [1995], Nesterov [1995], Luo [1996], and Kiwiel [1997b].”