Results 211 to 220 of about 3,986 (259)
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SIAM Journal on Optimization, 2002
Summary: We present an algorithm that achieves superlinear convergence for nonlinear programs satisfying the Mangasarian--Fromovitz constraint qualification and the quadratic growth condition. This convergence result is obtained despite the potential lack of a locally convex augmented Lagrangian.
Mihai Anitescu
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Summary: We present an algorithm that achieves superlinear convergence for nonlinear programs satisfying the Mangasarian--Fromovitz constraint qualification and the quadratic growth condition. This convergence result is obtained despite the potential lack of a locally convex augmented Lagrangian.
Mihai Anitescu
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Switching Stepsize Strategies for Sequential Quadratic Programming
Journal of Optimization Theory and Applications, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
George Tzallas-Regas, Berç Rustem
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Projected Sequential Quadratic Programming Methods
SIAM Journal on Optimization, 1996The author considers the optimization problem Minimize \(f(x)\) subject to \(c(x)=0\), \(a\leq u\leq b\) componentwise, where \(x=(y,u)\in \mathbb{R}^{m+n}\) and \(f:\mathbb{R}^{m+n} \to \mathbb{R}\), \(c: \mathbb{R}^{m+n}\to \mathbb{R}^m\) are sufficiently smooth. Such problems frequently arise in the numerical solution of optimal control problems. In
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A Feasible Trust-Region Sequential Quadratic Programming Algorithm [PDF]
SIAM Journal on Optimization, 2004 Summary: An algorithm for smooth nonlinear constrained optimization problems is described, in which a sequence of feasible iterates is generated by solving a trust-region sequential quadratic programming (SQP) subproblem at each iteration and by perturbing the resulting step to retain feasibility of each iterate. By retaining feasibility, the algorithm
Stephen J. Wright 0001, Matthew J. Tenny
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SQ2P, Sequential Quadratic Constrained Quadratic Programming
1998We follow the popular approach for unconstrained minimization, i.e. we develop a local quadratic model at a current approximate minimizer in conjunction with a trust region. We then minimize this local model in order to find the next approximate minimizer.
Serge Kruk, Henry Wolkowicz
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A Sequential Quadratically Constrained Quadratic Programming Method of Feasible Directions
Applied Mathematics and Optimization, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jian, Jin-bao +3 more
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Sequential quadratic programming for task plan optimization
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016We consider the problem of refining an abstract task plan into a motion trajectory. Task and motion planning is a hard problem that is essential to long-horizon mobile manipulation. Many approaches divide the problem into two steps: a search for a task plan and task plan refinement to find a feasible trajectory.
Dylan Hadfield-Menell +4 more
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Επαναληπτικός τετραγωνικός προγραμματισμός
2013The aim of this thesis is to study the solution of constrained nonlinear optimization problems using Sequential Quadratic Programming (SQP) method which has proved highly effective in practice. As with most optimization methods, SQP is not a single algorithm but rather a conceptual method from which numerous specific algorithms have evolved.
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Sequential Quadratic Programming for Parameter Identification Problems
IFAC Proceedings Volumes, 1989Abstract Sequential quadratic programming (SQP) is a technique for nonlinear equality constrained minimization problems, which, from the point of view of local convergence, is equivalent to finding a root of the gradient of the Lagrangian by Newton's method, if the second order sufficient conditions hold. For general, unstructured, finite dimensional
D.M. Hwang, C.T. Kelley
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Sequential Quadratic Programming Methods
2011In his 1963 PhD thesis, Wilson proposed the first sequential quadratic programming (SQP) method for the solution of constrained nonlinear optimization problems. In the intervening 48 years, SQP methods have evolved into a powerful and effective class of methods for a wide range of optimization problems. We review some of the most prominent developments
Philip E. Gill, Elizabeth Wong
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