A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm

By C. Lawrence, A. Tits. SIAM Journal on Optimization, 2000

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Volatility Transmission in Emerging European Foreign Exchange Markets (2011, methodology): “The DCC model is then fitted to the series of residuals, where the estimation is performed by optimizing the likelihood function using the Feasible Sequential Quadratic Programming (FSQP) algorithm of Lawrence and Tits (2001).”
Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory (2016, methodology): “Building on the framework developed in Part I, we propose a new class of convex approximation-based algorithms for the aforementioned two problems enjoying several desirable features.”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, methodology): “In our experiments, the Sequential Quadratic Programming (SQP) [54] is used to minimize the sequence of subproblems on the approximated landscape.”
Solving the unit commitment problem of hydropower plants via Lagrangian Relaxation and Sequential Quadratic Programming (2005, background): “We mention that more sophisticated alternatives would be possible, for instance considering active constraints as [23], or introducing slack variables in a trust-region SQP, as in [24].”
Evaluating Models of Autoregressive Conditional Duration (2006, methodology): “Maximum likelihood estimation of the ACD(1,1) model is performed using the sequential quadratic programming algorithm of Lawrence and Tits (2001) with analytical first derivatives.”