Procedures for optimization problems with a mixture of bounds and general linear constraints
ACM Transactions on Mathematical Software (TOMS)
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
Superlinearly convergent variable metric algorithms for general nonlinear programming problems.
Superlinearly convergent variable metric algorithms for general nonlinear programming problems.
A Globally Convergent Algorithm for Minimizing Over the Rotation Group of Quadratic Forms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond convexity: new perspectives in computational optimization
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
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In this note, we consider two of the major issues that have arisen in implementing a sequential quadratic programming (SQP) method for nonlinearly constrained optimization problems (the code NPSOL; Gill et al., 1983). The problem of concern is assumed to be of the form[EQUATION]where F(x) is a smooth nonlinear function, AL is a constant matrix, and c(x) is a vector of smooth nonlinear constraint functions. The matrix AL and the vector c(x) may be empty. Note that upper and lower bounds are specified for all the variables and for all the constraints. This from allows full generality in constraint specification. In particular, the i-th constraint may be defined as an equality by setting li = ui. If certain bounds are not present, the associated elements of l or u can be set to special values that will be treated as - ∞ or +∞.