Graph-based quadratic optimization: A fast evolutionary approach
Computer Vision and Image Understanding
Standard bi-quadratic optimization problems and unconstrained polynomial reformulations
Journal of Global Optimization
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We propose a first-order interior-point method for linearly constrained smooth optimization that unifies and extends first-order affine-scaling method and 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 experience on simplex constrained problems with 1000 variables is reported.