SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization

By Philip E. Gill, Walter Murray, Michael A. Saunders. SIAM Journal on Optimization, 2002

4,436 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Control and Systems Engineering, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/7f4901362e8f8fe603ad90d6aba5f5192ef8ea8b

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Citations in the corpus

Algorithms and Software for Convex Mixed Integer Nonlinear Programs (2012, neutral): “1 ha l-0 04 23 41 6, v er si on 2 - 15 O ct 2 00 9”
Truss topology optimization with discrete design variables—Guaranteed global optimality and benchmark examples (2007, methodology): “large-scaled nonlinear optimization as, e.g., Snopt ( Gill et al. 2002 ), realizing a method of sequential quadratic programming.”
Multimodality and Global Optimization in Aerodynamic Design (2013, neutral): “[24] SNOPTuses an SQP algorithm to find the solution to nonlinear optimization problems with general constraints.”
Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “The gradient-based optimizer (such as SLSQP [73] and SNOPT [74]) is provided through the pyOptSparse interface [75].”
On Augmented Lagrangian Methods with General Lower-Level Constraints (2007, methodology): “Most practical nonlinear programming methods published after 2001 rely on (a combination of) sequential quadratic programming (SQP), Newton-like, or barrier approaches [1, 5, 14, 16, 18, 19, 26, 27, 28, 29, 35, 38, 44, 45, 46, 48, 49, 50].”
Density Functions for Mesh Refinement in Numerical Optimal Control (2011, methodology): “In this implementation, we have used the optimization software SNOPT [12] for solving the corresponding nonlinear programming problem stemming from the discretized optimal control problem.”
Conditional volatility, skewness, and kurtosis: existence, persistence, and comovements (2003, methodology): “The estimations are made using a recent sophisticated sequential quadratic optimization algorithm implemented in SNOPT, developed by Gill et al. (1997, 1999).”
CUTEr and SifDec: A constrained and unconstrained testing environment, revisited (2003, methodology): “This package [Gill et al. 1998, 2002] minimizes a smooth linear or non-linear function subject to bounds and sparse linear or nonlinear constraints using SQP.”
Optimization as Estimation with Gaussian Processes in Bandit Settings (2015, methodology): “Then use SNOPT to solve the trajectory optimization problem, which involves an objective cost function (we take the negative cost to be a reward function to maximize), dynamics constraints, and obstacle constraints etc. Details of trajectory optimization are available in [32, 31]. w 4 6 8 g( w ) 0 0”
Optimal trajectory planning for trains – A pseudospectral method and a mixed integer linear programming approach (2013, methodology): “The resulting nonlinear programming problem is then solved by variants of well-developed algorithms, such as SNOPT (Gill et al., 2002) and IPOPT (Wächter and Biegler, 2006).”