Mathematical Programs with Optimization Problems in the Constraints

By J. Bracken, J. T. McGill. 1973

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Citations in the corpus

• Learning To Simulate (2018, background): “Attempting to solve it with a gradient-based approach poses multiple constraints on the lower-level problem 1b like smoothness, twice differentiability and an invertible Hessian (Bracken & McGill, 1973; Colson et al., 2007).”
• Bilevel Optimization and Machine Learning (2008, background): “These problems are closely related to the economic problem of the Stackelberg game, whose origin predates the work of Bracken and McGill.”
• Bilevel Optimization: Convergence Analysis and Enhanced Design (2020, methodology): “Bilevel optimization approaches : Bilevel optimization was first introduced by Bracken & McGill 1973.”
• Bilevel programming: A survey (2005, background): “Bilevel programs were initially considered by Bracken and McGill in a series of papers – see ken and McGill(1973)a (r), ken and McGill(1974)a (r), ken and McGill(1978)a (r) – that dealt with applications in the military field as well as in production and marketing decision making. By that time, such”
• A bilevel optimization approach to automated parameter tuning (2014, background): “Bracken and Mcgill [6] introduced bilevel programming to the domain of mathematical programming in the early seventies.”
• Approximation Methods for Bilevel Programming (2018, background): “This class of problems was first introduced by Bracken and McGill in 70’s [3].”
• An overview of bilevel optimization (2007, background): “Bilevel programs were initially considered by Bracken and McGill in a series of papers— see Bracken and McGill (1973, 1974, 1978)—that dealt with applications in the military field as well as in production and marketing decision making. By that time, such problems were called mathematical programs w”
• A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications (2017, background): “The second root is in the domain of mathematical programming, where the problems appeared as bilevel optimization problems containing a nested inner optimization problem as a constraint of an outer optimization problem [29].”