An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds
Mathematical Programming: Series A and B
A modified projection algorithm for large strictly-convex quadratic programs
Journal of Optimization Theory and Applications
Variable projection methods for large convex quadratic programs
Recent trends in numerical analysis
A parallel solver for large quadratic programs in training support vector machines
Parallel Computing - Special issue: Parallel computing in numerical optimization
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
On the convergence of the decomposition method for support vector machines
IEEE Transactions on Neural Networks
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We consider a parallel decomposition technique for solving the large quadratic programs arising in training the learning methodology Support Vector Machine. At each iteration of the technique a subset of the variables is optimized through the solution of a quadratic programming subproblem. This inner subproblem is solved in parallel by a special gradient projection method. In this paper we consider some improvements to the inner solver: a new algorithm for the projection onto the feasible region of the optimization subproblem and new linesearch and steplength selection strategies for the gradient projection scheme. The effectiveness of the proposed improvements is evaluated, both in terms of execution time and relative speedup, by solving large-scale benchmark problems on a parallel architecture.