期刊
MATHEMATICAL PROGRAMMING
卷 106, 期 1, 页码 25-57出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10107-004-0559-y
关键词
nonlinear programming; nonconvex constrained optimization; filter method; line search; interior-point method; Barrier method
We present a primal-dual interior-point algorithm with a filter line-search method for nonlinear programming. Local and global convergence properties of this method were analyzed in previous work. Here we provide a comprehensive description of the algorithm, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix. Heuristics are also considered that allow faster performance. This method has been implemented in the IPOPT code, which we demonstrate in a detailed numerical study based on 954 problems from the CUTEr test set. An evaluation is made of several line-search options, and a comparison is provided with two state-of-the-art interior-point codes for nonlinear programming.
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