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A first-order interior-point method for linearly constrained smooth optimization. (English) Zbl 1216.49028
Summary: We propose a first-order interior-point method for linearly constrained smooth optimization that unifies and extends the first-order affine-scaling method and the replicator dynamics method for standard quadratic programming. Global convergence and, in the case of quadratic program, (sub)linear convergence rate and iterate convergence results are derived. Numerical experiences on simplex constrained problems with 1000 variables are presented.

49M37 Numerical methods based on nonlinear programming
65K05 Numerical mathematical programming methods
90C20 Quadratic programming
90C25 Convex programming
90C26 Nonconvex programming, global optimization
90C30 Nonlinear programming
90C51 Interior-point methods
MINOS; minpack
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