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ID | Category | Severity | Reproducibility | Date Submitted | Last Update | |||||||
0000437 | [ALGLIB] Optimization | feature | have not tried | 2012-01-17 15:09 | 2018-01-02 17:22 | |||||||
Reporter | SergeyB | View Status | public | |||||||||
Assigned To | SergeyB | |||||||||||
Priority | normal | Resolution | open | Platform | ||||||||
Status | assigned | OS | ||||||||||
Projection | none | OS Version | ||||||||||
ETA | none | Fixed in Version | Product Version | |||||||||
Target Version | Next 'Optimization' release | Product Build | ||||||||||
Summary | 0000437: Feasibility solver | |||||||||||
Description |
Solver which finds feasible point subject to boundary and linear constraints: * two forms - canonical (A*x=b) and general BLEIC constraints * may be, solver for a problem with general (nonlinear) constraints * scaling of the inputs and constraint matrix - we must rescale problem in order to have good convergence * use LQ or bidiagonalization instead of SVD. Or, maybe, orthogonalization for rows of the matrix... * use algorithm similar to one used by MinQP. Not sure that we should use MinQP internally, because MinQP may need same feasibility detection code. Maybe we should separate internal QP solver from MinQP subpackage, and call this solver from feasibility solver AND from MinQP * efficient hot starts |
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Steps To Reproduce | ||||||||||||
Additional Information | ||||||||||||
Programming language | Unspecified | |||||||||||
Attached Files | ||||||||||||
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