The MOSEK Python optimizer API manual Version 7.0 (Revision 141)
Optimizer API for Python - Documentation - Mosek Optimizer API for Python - Documentation - Mosek
270 APPENDIX A. API REFERENCE A.2.129 Task.getsolsta() solsta = Task.getsolsta(whichsol) Obtains the solution status. Arguments solsta : solsta Solution status. whichsol : soltype Selects a solution. Description: Obtains the solution status. A.2.130 Task.getsolution() prosta,solsta = Task.getsolution( whichsol, skc, skx, skn, xc, xx, y, slc, suc, slx, sux, snx) Obtains the complete solution. Arguments prosta : prosta Problem status. skc : mosek.stakey[] Status keys for the constraints. skn : mosek.stakey[] Status keys for the conic constraints. skx : mosek.stakey[] Status keys for the variables.
A.2. CLASS TASK 271 slc : double[] Dual variables corresponding to the lower bounds on the constraints. slx : double[] Dual variables corresponding to the lower bounds on the variables. snx : double[] Dual variables corresponding to the conic constraints on the variables. solsta : solsta Solution status. suc : double[] Dual variables corresponding to the upper bounds on the constraints. sux : double[] Dual variables corresponding to the upper bounds on the variables. whichsol : soltype Selects a solution. xc : double[] Primal constraint solution. xx : double[] Primal variable solution. y : double[] Vector of dual variables corresponding to the constraints. Description: Obtains the complete solution. Consider the case of linear programming. The primal problem is given by and the corresponding dual problem is minimize c T x + c f subject to l c ≤ Ax ≤ u c , l x ≤ x ≤ u x . maximize (l c ) T s c l − (u c ) T s c u + (l x ) T s x l − (u x ) T s x u + c f subject to A T y + s x l − s x u = c, − y + s c l − s c u = 0, s c l , s c u, s x l , s x u ≥ 0. In this case the mapping between variables and arguments to the function is as follows: xx: Corresponds to variable x.
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A.2. CLASS TASK 271<br />
slc : double[]<br />
Dual variables corresponding to the lower bounds on the constraints.<br />
slx : double[]<br />
Dual variables corresponding to the lower bounds on the variables.<br />
snx : double[]<br />
Dual variables corresponding to the conic constraints on the variables.<br />
solsta : solsta<br />
Solution status.<br />
suc : double[]<br />
Dual variables corresponding to the upper bounds on the constraints.<br />
sux : double[]<br />
Dual variables corresponding to the upper bounds on the variables.<br />
whichsol : soltype<br />
Selects a solution.<br />
xc : double[]<br />
Primal constraint solution.<br />
xx : double[]<br />
Primal variable solution.<br />
y : double[]<br />
Vector of dual variables corresponding to the constraints.<br />
Description:<br />
Obtains the complete solution.<br />
Consider the case of linear programming. <strong>The</strong> primal problem is given by<br />
and the corresponding dual problem is<br />
minimize<br />
c T x + c f<br />
subject to l c ≤ Ax ≤ u c ,<br />
l x ≤ x ≤ u x .<br />
maximize (l c ) T s c l − (u c ) T s c u<br />
+ (l x ) T s x l − (u x ) T s x u + c f<br />
subject to A T y + s x l − s x u = c,<br />
− y + s c l − s c u = 0,<br />
s c l , s c u, s x l , s x u ≥ 0.<br />
In this case the mapping between variables and arguments to the function is as follows:<br />
xx:<br />
Corresponds to variable x.
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