Hierarchical optimization: An introduction

By Gnana Anandalingam, Terry L. Friesz. 1992

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Citations in the corpus

A Comprehensive Survey of Neural Architecture Search (2020, neutral): “By solving a bilevel optimization problem [66, 67] to jointly optimize the probability of mixed operations (the parameters α of the network architecture) and network weightsw :”
A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications (2017, background): “For more complex bilevel problems, the readers may refer [10] and [48].”
StacNAS: Towards stable and consistent optimization for differentiable Neural Architecture Search (2019, methodology): “…formulated the optimization of the architecture parameters α and the weights w associated with the architecture as a bi-level optimization problem (Anandalingam & Friesz (1992)), where α is treated as the upper-level variable and w as the lower-level variable: min α Lval(w ∗(α), α) (3) s.t.…”
Human preferences for robot-human hand-over configurations (2011, background): “defining a series of optimization problems that are solved in a predetermined order [2].”
DARTS: Differentiable Architecture Search (2018, methodology): “This implies a bilevel optimization problem (Anandalingam and Friesz, 1992; Colson et al., 2007) with α as the upper-level variable and w as the lower-level variable: The nested formulation also arises in gradient-based hyperparameter optimization (Maclaurin et al., 2015; Pedregosa, 2016), which is…”
An overview of bilevel optimization (2007, background): “Hence more general surveys appeared, such as those by Savard (1989), Anandalingam and Friesz (1992) and Vicente and Calamai (1994). Colson (1999) deals with both nonlinear bilevel programming problems and mathematical programs with equilibrium constraints and recently Dempe (2003) wrote an annotated”