[
    {
        "id": "osp-13550",
        "type": "article-journal",
        "title": "Global Search Strategies for Solving Multilinear Least-Squares Problems",
        "author": [
            {
                "family": "Andersson",
                "given": "Mats"
            },
            {
                "family": "Burdakov",
                "given": "Oleg P."
            },
            {
                "family": "Knutsson",
                "given": "Hans E."
            },
            {
                "family": "Zikrin",
                "given": "Spartak"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/global-search-strategies-for-solving-multilinear-least-squares-problems",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2012
                ]
            ]
        },
        "container-title": "Sultan Qaboos University Journal for Science",
        "volume": "16",
        "page": "12",
        "DOI": "10.24200/squjs.vol17iss1pp12-21",
        "publisher": "Sultan Qaboos University",
        "ISSN": "2308-3921",
        "abstract": "The multilinear least-squares (MLLS) problem is an extension of the linear least-squares problem. The difference is that a multilinear operator is used in place of a matrix-vector product. The MLLS is typically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows for moving from one local minimizer to a better one. The efficiency of this strategy is illustrated by the results of numerical experiments performed for some problems related to the design of filter networks."
    }
]