Abstract
We present a modification of Karmarkar's linear programming algorithm. Our algorithm uses a recentered projected gradient approach thereby obviating a priori knowledge of the optimal objective function value. Assuming primal and dual nondegeneracy, we prove that our algorithm converges. We present computational comparisons between our algorithm and the revised simplex method. For small, dense constraint matrices we saw little difference between the two methods.
Original language | English (US) |
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Pages (from-to) | 395-407 |
Number of pages | 13 |
Journal | Algorithmica |
Volume | 1 |
Issue number | 1-4 |
DOIs | |
State | Published - Nov 1986 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Computer Science
- Applied Mathematics
- Computer Science Applications
Keywords
- Karmarkar's algorithm
- Least squares
- Linear programming
- Projected gradient methods