# A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Instances** & **Scenarios** & **cp Multiplier** & **Iterations** & **Time** & **UB** \\ \hline \multirow{8}{*}{DCAP 233} & \multirow{8}{*}{200} & cp 0.1 & \(>\)300 & 456.18 & 2206.7 \\  & & cp 0.3 & \(>\)300 & 457.5 & 2431.8 \\  & & cp 0.5 & \(>\)300 & 458.23 & 1966.1 \\  & & cp 1.0 & \(>\)300 & 455.88 & 1952.6 \\ \cline{2-6}  & \multirow{8}{*}{300} & cp 0.1 & \(>\)300 & 734.23 & 1862.0 \\  & & cp 0.3 & \(>\)300 & 726.59 & 1943.3 \\  & & cp 0.5 & \(>\)300 & 726.77 & 1831.6 \\  & & cp 1.0 & \(>\)300 & 741.17 & 1815.4 \\ \cline{2-6}  & \multirow{8}{*}{500} & cp 0.1 & \(>\)300 & 1515.27 & 2498.1 \\  & & cp 0.3 & \(>\)300 & 1492.59 & 2000.0 \\  & & cp 0.5 & \(>\)300 & 1467.64 & 1939.2 \\  & & cp 1.0 & \(>\)300 & 1494.13 & 1893.8 \\ \hline \multirow{8}{*}{DCAP 243} & \multirow{8}{*}{300} & cp 0.1 & \(>\)300 & 481.96 & 2465.9 \\  & & cp 0.3 & \(>\)300 & 478.34 & 2454.2 \\  & & cp 0.5 & \(>\)300 & 481.51 & 2369.3 \\  & & cp 1.0 & \(>\)300 & 466.95 & 2383.2 \\ \cline{2-6}  & \multirow{8}{*}{300} & cp 0.1 & \(>\)300 & 792.01 & 2825.8 \\  & & cp 0.3 & \(>\)300 & 756.17 & 2802.8 \\  & & cp 0.5 & \(>\)300 & 710.44 & 2755.0 \\  & & cp 1.0 & \(>\)300 & 776.97 & 2743.1 \\ \cline{2-6}  & \multirow{8}{*}{500} & cp 0.1 & \(>\)300 & 1690.74 & 2196.0 \\  & & cp 0.3 & \(>\)300 & 1622.8 & 2235.2 \\  & & cp 0.5 & \(>\)300 & 1674.76 & 2216.7 \\  & & cp 1.0 & \(>\)300 & 1536.42 & 2323.3 \\ \hline \multirow{8}{*}{DCAP 332} & \multirow{8}{*}{300} & cp 0.1 & \(>\)300 & 456.58 & 1362.7 \\  & & cp 0.3 & \(>\)300 & 427.65 & 1529.1 \\ \cline{1-1}  & & cp 0.5 & \(>\)300 & 450.88 & 1278.0 \\ \cline{1-1}  & & cp 1.0 & \(>\)300 & 460.35 & 1171.0 \\ \cline{1-1} \cline{2-6}  & \multirow{8}{*}{500} & cp 0.1 & \(>\)300 & 714.83 & 1332.1 \\ \cline{1-1}  & & cp 0.3 & \(>\)300 & 698.52 & 1948.9 \\ \cline{1-1}  & & cp 0.5 & \(>\)300 & 709.21 & 1904.4 \\ \cline{1-1}  & & cp 1.0 & \(>\)300 & 706.75 & 1766.3 \\ \cline{1-1} \cline{2-6}  & \multirow{8}{*}{500} & cp 0.1 & \(>\)300 & 1464.11 & 1768.6 \\ \cline{1-1}  & & cp 0.3 & \(>\)300 & 1451.73 & 1822.8 \\ \cline{1-1}  & & cp 0.5 & \(>\)300 & 1473.66 & 1846.6 \\ \cline{1-1}  & & cp 1.0 & \(>\)300 & 1452.64 & 1861.6 \\ \hline \multirow{8}{*}{DCAP 342} & \multirow{8}{*}{300} & cp 0.1 & \(>\)300 & 449.9 & 1993.4 \\ \cline{1-1}  & & cp 0.3 & \(>\)300 & 476.77 & 1990.4 \\ \cline{1-1}  & & cp 0.5 & \(>\)300 & 445.11 & 1870.6 \\ \cline{1-1}  & & cp 1.0 & \(>\)300 & 470.82 & 1830.8 \\ \cline{1-1} \cline{2-6}  & \multirow{8}{*}{500} & cp 0.1 & \(>\)300 & 722.6 & 2260.3 \\ \cline{1-1}  & & cp 0.3 & \(>\)300 & 739.94 & 2371.1 \\ \cline{1-1}  & & cp 0.5 & \(>\)300 & 690.76 & 2497.7 \\ \cline{1-1}  & & cp 1.0 & \(>\)300 & 702.23 & 2255.9 \\ \cline{1-1} \cline{2-6}  & \multirow{8}{*}{500} & cp 0.1 & \(>\)300 & 1582.9 & 2198.1 \\ \cline{1-1}  & & cp 0.3 & \(>\)300 & 1604.98 & 2317.5 \\ \cline{1-1}  & & cp 0.5 & \(>\)300 & 1555.9 & 2290.6 \\ \cline{1-1}  & & cp 1.0 & \(>\)300 & 1593.41 & 2097.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational results for PySP on DCAP. The SAPHIR collection consists of five instances of the optimization of a gas-purchase portfolio, considering the cost of purchase, as well as underground storage capacities and transportation, under uncertain demand conditions [84]. In this family of problems, the random elements are located in both the RHS and constraint matrices \(W_{\omega}\) and \(T_{\omega}\). The sizes of all of the test problems are shown in Table 10. The size is expressed as the number of constraints (Rows) and variables (Cols) in the first stage and the second stage per scenario. None of the test instances consider integer variables in the first-stage.