Affine scaling method for solving quadratic programming problem (QP) via diagonal approximation is presented in this paper. The algorithm starts from a strictly feasible interior point of the constrained region and a suitable diagonal matrix is chosen to approximate the Hessian matrix of the quadratic programming problem, and a new quadratic programming problem (DQP) associated with the diagonal matrix is obtained. A new iterative interior point is generated by solving the DQP problem in terms of the affine scaling algorithm. The convergence of the method is proven under the assumptions that the feasible region is bounded and teh original QP problem is not degenerate. A global optimal solution can be obtained for the convex QP problem, and a stationary point can be generated for the general QP problem. A similar data structure of linear programming interior point methods can be used to implement the algorithm for solving large sparse problem.
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