An Algorithm for Restricted Least Squares Regression
作者： Richard L. Dykstra
DOI: 10.1080/01621459.1983.10477029
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摘要: Abstract A commonly occurring problem in statistics is that of minimizing a least squares expression subject to side constraints. Here simple iterative algorithm presented and shown converge the desired solution. Several examples are presented, including finding closest concave (convex) function set points other general quadratic programming problems. The dual basic also discussed solution for it given terms algorithm. Finally, extensions expressions than given.
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