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The present paper is
devoted to a novel smoothing function method for convex quadratic programming
problem with mixed constrains, which has important application in mechanics and
engineering science. The problem is reformulated as a system of non-smooth equations, and then a smoothing function for the system of
non-smooth equations is
proposed. The condition of convergences of this iteration algorithm is given.
Theory analysis and primary numerical results illustrate that this method is
feasible and effective.
This paper considers a new canonical duality theory for solving mixed
integer quadratic programming problem. It shows that this well-known NP-hard
problem can be converted into concave maximization dual problems without duality
gap. And the dual problems can be solved, under certain conditions, by polynomial algorithms.