Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
The smallest point of a polytope
Journal of Optimization Theory and Applications
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The paper describes numerical experiments with active set methods for solving bounded linear least squares problems. It concentrates on two problems that arise in the implementation of the active set method. One problem is the choice of a good starting point. The second problem is how to move out of a “dead point.” The paper investigates the use of simple iterative methods to solve these problems. The results of our experiments indicate that the use of Gauss-Seidel iterations to obtain a starting point is likely to provide large gains in efficiency. Another interesting conclusion is that dropping one constraint at a time is advantageous to dropping several constraints at a time.