Name
Abstract | ||
|---|---|---|
1 , the smallest eigenvalue of a symmetric, positive definite matrix, and is solved by Newton iteration with line search. The
paper describes the algorithm and its implementation including estimation of λ1, how to get a good starting point for the iteration, and up- and downdating of Cholesky factorization. Results of extensive
testing and comparison with other methods for constrained QP are given.
|
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/s101070050049 | Math. Program. |
Keywords | Field | DocType |
Key words: bound constrained quadratic programming – Huber’s M–estimator – condition estimation – Newton iteration – factorization update | Mathematical optimization,Incomplete Cholesky factorization,Positive-definite matrix,Quadratic function,Factorization,Quadratic programming,Mathematics,Power iteration,Newton's method,Cholesky decomposition | Journal |
Volume | Issue | ISSN |
85 | 1 | 0025-5610 |
Citations | PageRank | References |
6 | 1.27 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaj Madsen | 1 | 341 | 63.86 |
| Hans Bruun Nielsen | 2 | 32 | 7.14 |
| Mustafa Ç. Pınar | 3 | 139 | 14.88 |