ISSN:
1573-2878
Keywords:
Primal-dual methods
;
Lagrange functions
;
decomposition
;
nonlinear programming
;
convergence analysis
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mathematics
Notes:
Abstract A new algorithm for solving nonconvex, equality-constrained optimization problems with separable structures is proposed in the present paper. A new augmented Lagrangian function is derived, and an iterative method is presented. The new proposed Lagrangian function preserves separability when the original problem is separable, and the property of linear convergence of the new algorithm is also presented. Unlike earlier algorithms for nonconvex decomposition, the convergence ratio for this method can be made arbitrarily small. Furthermore, it is feasible to extend this method to algorithms suited for inequality-constrained optimization problems. An example is included to illustrate the method.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00940477
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