Practical optimization

By Philip E. Gill, Walter Murray, Margaret H. Wright. 2019

7,600 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/a4e414dfbd9bb425516f866c57213ca43dd54260

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In Artificial Intelligence

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Citations in the corpus

A functional model based statistical time series method for vibration based damage detection, localization, and magnitude estimation (2013, methodology): “the first one realized via a hybrid optimization scheme based on genetic algorithms (GA) [47] and constrained nonlinear optimization (sequential quadratic programming—SQP) [48,49].”
Low-Thrust Transfers in the Earth-Moon System, Including Applications to Libration Point Orbits (2010, neutral): “̇̂ v = ~̇v / v − ~vv̇ / v(2), (43) ̇̂ h = ~̇h / h−~hḣ / h(2), (44) ̇̂ w = ̇̂ h× v̂ + ĥ× ̇̂ v.”
Kalman filtering with inequality constraints for turbofan engine health estimation (2006, background): “The problem de ̄ned by (23) is known as a quadratic programming problem [14, 15].”
Thin plate regression splines (2003, background): “Now find any orthogonal column basis Zk such that T'UkZk = 0 (QR- or QT-factorization will provide this easily; see for example Gill et al. (1981)).”
R8s: Inferring Absolute Rates of Molecular Evolution, Divergence times in the Absence of a Molecular Clock (2003, methodology): “Older versions of r8s implemented bound constraints using general nonlinear constraint methods such as barrier/penalty functions (Gill et al., 1981), with optimization relying on Powell’s method without gradients.”
A Three-Dimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars (2004, neutral): “…where n is the number of iterations, (cid:98) is an optimal step size obtained by the so-called line search process in the optimal control theory (Gill et al. 1981), and f ( (cid:93) J / (cid:93) q ) is the optimal descent direction obtained by combining the gradients from several previous…”
Imposing Hard Constraints on Deep Networks: Promises and Limitations (2017, background): “It has been shown that, in many cases, replacing the soft constraints by hard ones addresses both of these problems [5, 8]: The optimizer automatically chooses the relative weights and the constraints end up being satisfied to machine precision if a feasible solution exists.”
Distance Metric Learning: A Comprehensive Survey (2006, neutral): “24 4.3.1 LLE(Locally Linear Embedding) . . . . . . . . . . . . . . . 24 4.3.2 ISOMAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.3.3 Laplacian Eigenmaps . . . . . . . . . . . . . . . . . . . . .”
Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications (2000, methodology): “Osowski et al. (1996) applied the efﬁcient, limited memory BFGS quasi-Newton method (Gill et al., 1981) to feedforward networks.”