Linear and Nonlinear Optimization

By Igor Griva, Stephen G. Nash, A. Sofer. 2008

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Evasion and Hardening of Tree Ensemble Classifiers (2015, background): “This reduction avoids introducing constraints with so called “big-M” constants (Griva et al., 2008) at the cost of a slightly more complex solution encoding.”
Mixed-integer nonlinear optimization*† (2013, background): “1), then A3 follows from A2 as a consequence of Slater’s constraint qualification (Griva et al., 2009), which is one of the strongest constraint qualifications.”
Variational quantum algorithms for nonlinear problems (2019, methodology): “⟪ ⟫ = ⟨ ⟩ ⟪ ⟫ = ( − ⟨ ⟩ )/ Furthermore, derivatives of the cost function, as required by some minimization algorithms [36, 55, 56], can be evaluated by combining the ideas presented here with the quantum circuits discussed in [24, 30, 57, 58].”
Convergence rates of sub-sampled Newton methods (2015, background): “To the best of our knowledge, the best result in this direction is proven in [BCNN11] that estabilishes asymptotic convergence without quantitative bounds (exploiting general theory from [GNS09]).”
Safe Control under Uncertainty (2015, unknown): “Prior work shows that MPC optimization with STL constraints φ can be posed as a Mixed Integer Linear Program (MILP) [34, 17].”