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75
Snopt: An SQP Algorithm For LargeScale Constrained Optimization
, 1997
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 345 (19 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available, and that the constraint gradients are sparse.
GLOBAL CONVERGENCE PROPERTIES OF CONJUGATE GRADIENT METHODS FOR OPTIMIZATION
, 1992
"... This paper explores the convergence ofnonlinear conjugate gradient methods without restarts, and with practical line searches. The analysis covers two classes ofmethods that are globally convergent on smooth, nonconvex functions. Some properties of the FletcherReeves method play an important role ..."
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Cited by 71 (2 self)
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This paper explores the convergence ofnonlinear conjugate gradient methods without restarts, and with practical line searches. The analysis covers two classes ofmethods that are globally convergent on smooth, nonconvex functions. Some properties of the FletcherReeves method play an important role in the first family, whereas the second family shares an important property with the PolakRibire method. Numerical experiments are presented.
On the resolution of monotone complementarity problems
 Comput. Optim. Appl
, 1996
"... Abstract. A reformulation of the nonlinear complementarity problem (NCP) as an unconstrained minimization problem is considered. It is shown that any stationary point of the unconstrained objective function is already a solution of NCP if the mapping F involved in NCP is continuously differentiable ..."
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Cited by 44 (8 self)
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Abstract. A reformulation of the nonlinear complementarity problem (NCP) as an unconstrained minimization problem is considered. It is shown that any stationary point of the unconstrained objective function is already a solution of NCP if the mapping F involved in NCP is continuously differentiable and monotone. A descent algorithm is described which uses only function values of F. Some numerical results are given.
On the implementation of an algorithm for largescale equality constrained optimization
 SIAM Journal on Optimization
, 1998
"... Abstract. This paper describes a software implementation of Byrd and Omojokun’s trust region algorithm for solving nonlinear equality constrained optimization problems. The code is designed for the efficient solution of large problems and provides the user with a variety of linear algebra techniques ..."
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Cited by 40 (11 self)
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Abstract. This paper describes a software implementation of Byrd and Omojokun’s trust region algorithm for solving nonlinear equality constrained optimization problems. The code is designed for the efficient solution of large problems and provides the user with a variety of linear algebra techniques for solving the subproblems occurring in the algorithm. Second derivative information can be used, but when it is not available, limited memory quasiNewton approximations are made. The performance of the code is studied using a set of difficult test problems from the CUTE collection.
Applications of Multidimensional Scaling to Molecular Conformation
, 1997
"... Multidimensional scaling (MDS) is a collection of data analytic techniques for constructing configurations of points from information about interpoint distances. Such constructions arise in computational chemistry when one endeavors to infer the conformation (3dimensional structure) of a molecule fr ..."
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Cited by 22 (5 self)
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Multidimensional scaling (MDS) is a collection of data analytic techniques for constructing configurations of points from information about interpoint distances. Such constructions arise in computational chemistry when one endeavors to infer the conformation (3dimensional structure) of a molecule from information about its interatomic distances. For a number of reasons, this application of MDS poses computational challenges not encountered in more traditional applications. In this report we sketch the mathematical formulation of MDS for molecular conformation problems and describe two approaches that can be employed for their solution. 1 Molecular Conformation Consider a molecule with n atoms. We can represent its conformation, or 3dimensional structure, by specifying the coordinates of each atom with respect to a Euclidean coordinate system for ! 3 . We store these coordinates in an n \Theta 3 configuration matrix X. Given X, we can easily compute the matrix of interatomic distan...
A Family of Variable Metric Proximal Methods
, 1993
"... We consider conceptual optimization methods combining two ideas: the MoreauYosida regularization in convex analysis, and quasiNewton approximations of smooth functions. We outline several approaches based on this combination, and establish their global convergence. Then we study theoretically the ..."
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Cited by 22 (2 self)
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We consider conceptual optimization methods combining two ideas: the MoreauYosida regularization in convex analysis, and quasiNewton approximations of smooth functions. We outline several approaches based on this combination, and establish their global convergence. Then we study theoretically the local convergence properties of one of these approaches, which uses quasiNewton updates of the objective function itself. Also, we obtain a globally and superlinearly convergent BFGS proximal method. At each step of our study, we single out the assumptions that are useful to derive the result concerned.
Numerical experience with limitedMemory QuasiNewton methods and Truncated Newton methods
 SIAM J. Optimization
, 1992
"... Abstract. Computational experience with several limitedmemory quasiNewton and truncated Newton methods for unconstrained nonlinear optimization is described. Comparative tests were conducted on a wellknown test library [J. J. Mor, B. S. Garbow, and K. E. Hillstrom, ACM Trans. Math. Software, 7 (1 ..."
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Cited by 13 (9 self)
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Abstract. Computational experience with several limitedmemory quasiNewton and truncated Newton methods for unconstrained nonlinear optimization is described. Comparative tests were conducted on a wellknown test library [J. J. Mor, B. S. Garbow, and K. E. Hillstrom, ACM Trans. Math. Software, 7 (1981), pp. 1741], on several synthetic problems allowing control of the clustering of eigenvalues in the Hessian spectrum, and on some largescale problems in oceanography and meteorology. The results indicate that among the tested limitedmemory quasiNewton methods, the LBFGS method [D. C. Liu and J. Nocedal, Math. Programming, 45 (1989), pp. 503528] has the best overall performance for the problems examined. The numerical performance of two truncated Newton methods, differing in the innerloop solution for the search vector, is competitive with that of LBFGS. Key words, limitedmemory quasiNewton methods, truncated Newton methods, synthetic cluster functions, largescale unconstrained minimization AMS subject classifications. 90C30, 93C20, 93C75, 65K10, 76C20 1. Introduction. Limitedmemory quasiNewton (LMQN) and truncated Newton
Kullback Proximal Algorithms for Maximum Likelihood Estimation
 IEEE Transactions on Information Theory
, 1998
"... Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization metho ..."
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Cited by 13 (4 self)
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Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum likelihood estimates and study its convergence properties. These methods are based on a proximal point algorithm implemented with the KullbackLiebler (KL) divergence between posterior densities of the complete data as a proximal penalty function. When the proximal relaxation parameter is set to unity one obtains the classical expectation maximization (EM) algorithm. For a decreasing sequence of relaxation parameters, relaxed versions of EM are obtained which can have much faster asymptotic convergence without sacrice of monotonicity. We present an implementation of the algorithm using More's Trust Region update strategy. For illustration the method is applied to a...
A feasible BFGS interior point algorithm for solving strongly convex minimization problems
 SIAM J. OPTIM
, 2000
"... We propose a BFGS primaldual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of posit ..."
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Cited by 13 (1 self)
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We propose a BFGS primaldual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of positive parameters µ converging to zero. We prove that it converges qsuperlinearly for each fixed µ. We also show that it is globally convergent to the analytic center of the primaldual optimalset when µ tends to 0 and strict complementarity holds.