The MOSEK Python optimizer API manual Version 7.0 (Revision 141)
Optimizer API for Python - Documentation - Mosek Optimizer API for Python - Documentation - Mosek
622 APPENDIX G. PROBLEM ANALYZER EXAMPLES G.3 Problem with both linear and quadratic constraints Analyzing the problem Constraints Bounds Variables lower bd: 40 upper bd: 1 cont: all upper bd: 121 fixed : 204 fixed : 5480 free : 5600 ranged : 161 ranged : 40 ------------------------------------------------------------------------------- Objective, maximize cx range: all |c| in {0.00000, 15.4737} distrib: |c| vars 0 5844 15.4737 1 ------------------------------------------------------------------------------- Constraint matrix A has 5802 rows (constraints) 5845 columns (variables) 6480 (0.0191079%) nonzero entries (coefficients) Row nonzeros, A i range: min A i: 0 (0%) max A i: 3 (0.0513259%) distrib: A i rows rows% acc% 0 80 1.38 1.38 1 5003 86.23 87.61 2 680 11.72 99.33 3 39 0.67 100.00 0/80 empty rows have quadratic terms Column nonzeros, A|j range: min A|j: 0 (0%) max A|j: 15 (0.258532%) distrib: A|j cols cols% acc% 0 204 3.49 3.49 1 5521 94.46 97.95 2 40 0.68 98.63 [3, 7] 40 0.68 99.32 [8, 15] 40 0.68 100.00 0/204 empty columns correspond to variables used in conic and/or quadratic expressions only A nonzeros, A(ij) range: min |A(ij)|: 2.02410e-05 max |A(ij)|: 35.8400 distrib: A(ij) coeffs [2.02e-05, 0.0001) 40 [0.0001, 0.001) 118 [0.001, 0.01) 305 [0.01, 0.1) 176 [0.1, 1) 40 [1, 10) 5721 [10, 35.8] 80 -------------------------------------------------------------------------------
G.4. PROBLEM WITH BOTH LINEAR AND CONIC CONSTRAINTS 623 Constraint bounds, lb
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- Page 635 and 636: F.3. THE OPF FORMAT 613 [bounds] [b
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622 APPENDIX G. PROBLEM ANALYZER EXAMPLES<br />
G.3 Problem with both linear and quadratic constraints<br />
Analyzing the problem<br />
Constraints Bounds Variables<br />
lower bd: 40 upper bd: 1 cont: all<br />
upper bd: 121 fixed : 204<br />
fixed : 5480 free : 5600<br />
ranged : 161 ranged : 40<br />
-------------------------------------------------------------------------------<br />
Objective, maximize cx<br />
range: all |c| in {0.00000, 15.4737}<br />
distrib: |c| vars<br />
0 5844<br />
15.4737 1<br />
-------------------------------------------------------------------------------<br />
Constraint matrix A has<br />
5802 rows (constraints)<br />
5845 columns (variables)<br />
6480 (0.0191079%) nonzero entries (coefficients)<br />
Row nonzeros, A i<br />
range: min A i: 0 (0%) max A i: 3 (0.0513259%)<br />
distrib: A i rows rows% acc%<br />
0 80 1.38 1.38<br />
1 5003 86.23 87.61<br />
2 680 11.72 99.33<br />
3 39 0.67 100.00<br />
0/80 empty rows have quadratic terms<br />
Column nonzeros, A|j<br />
range: min A|j: 0 (0%) max A|j: 15 (0.258532%)<br />
distrib: A|j cols cols% acc%<br />
0 204 3.49 3.49<br />
1 5521 94.46 97.95<br />
2 40 0.68 98.63<br />
[3, 7] 40 0.68 99.32<br />
[8, 15] 40 0.68 100.00<br />
0/204 empty columns correspond to variables used in conic<br />
and/or quadratic expressions only<br />
A nonzeros, A(ij)<br />
range: min |A(ij)|: 2.02410e-05 max |A(ij)|: 35.8400<br />
distrib: A(ij) coeffs<br />
[2.02e-05, 0.0001) 40<br />
[0.0001, 0.001) 118<br />
[0.001, 0.01) 305<br />
[0.01, 0.1) 176<br />
[0.1, 1) 40<br />
[1, 10) 5721<br />
[10, 35.8] 80<br />
-------------------------------------------------------------------------------