An interior algorithm for nonlinear optimization that combines line search and trust region steps
摘要：
An interior-point method for nonlinear programming is presented. It enjoys the flexibility of switching between a line search method that computes steps by factoring the primal-dual equations and a trust region method that uses a conjugate gradient iteration. Steps computed by direct factorization are always tried first, but if they are deemed ineffective, a trust region iteration that guarantees progress toward stationarity is invoked. To demonstrate its effectiveness, the algorithm is implemented in the Knitro [6,28] software package and is extensively tested on a wide selection of test problems.
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关键词：
UNCONSTRAINED TESTING ENVIRONMENT Q-SUPERLINEAR CONVERGENCE CONSTRAINED OPTIMIZATION POINT ALGORITHM
DOI：
10.1007/s10107-004-0560-5
被引量：
年份：
2006
Springer
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