The MOSEK Python optimizer API manual Version 7.0 (Revision 141)

192 CHAPTER 15. SENSITIVITY ANALYSIS
Basis type
Var. β 1 β 2 σ 1 σ 2
c 1 −∞ 3.00 300.00 300.00
c 2 −∞ ∞ 100.00 100.00
c 3 −2.00 ∞ 0.00 0.00
c 4 −∞ 2.00 500.00 500.00
c 5 −3.00 ∞ 500.00 500.00
c 6 −∞ 2.00 500.00 500.00
c 7 −2.00 ∞ 0.00 0.00
Optimal partition type
Var. β 1 β 2 σ 1 σ 2
c 1 −∞ 3.00 300.00 300.00
c 2 −∞ ∞ 100.00 100.00
c 3 −2.00 ∞ 0.00 0.00
c 4 −∞ 2.00 500.00 500.00
c 5 −3.00 ∞ 500.00 500.00
c 6 −∞ 2.00 500.00 500.00
c 7 −2.00 ∞ 0.00 0.00
Table 15.2: Ranges and shadow prices related to the objective coefficients. Left: Results for the basis
type sensitivity analysis. Right: Results for the optimal partition type sensitivity analysis.
β ∈ [0, 300]
then the optimal objective value will increase by the value
σ 1 β = 3β.
15.5 Sensitivity analysis from the MOSEK API
MOSEK provides the functions Task.primalsensitivity and Task.dualsensitivity for performing
sensitivity analysis. The code below gives an example of its use.
2 ##
[ sensitivity.py ]
3 # Copyright: Copyright (c) MOSEK ApS, Denmark. All rights reserved.
4 #
5 # File: sensitivity.py
6 #
7 # Purpose: To demonstrate how to perform sensitivity
8 # analysis from the API on a small problem:
9 #
10 # minimize
11 #
12 # obj: +1 x11 + 2 x12 + 5 x23 + 2 x24 + 1 x31 + 2 x33 + 1 x34
13 # st
14 # c1: + x11 + x12