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An accelerated preconditioned primal-dual gradient algorithm for structured nonconvex optimization problems
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DOI：10.1016/j.cnsns.2025.109480.png)
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Abstract
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• An novel accelerated preconditioned primal-dual gradient algorithm for solving nonconvex optimization problems by the conjugate duality theory of nonconvex functions. • Our algorithm only needs to calculate the proximal mapping of the conjugate function which is always convex and lower semicontinuous and it does not need to calculate the proximal mapping of nonconvex functions. the computation load may be significantly reduced. • Global convergence under Kurdyka-Lojasiewicz condition. • Numerical results illustrate that the proposed algorithm is quite competitive with some existing algorithms.
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