### Table 1: Node counts and time for instances of multi-commodity network ow problems CPLEX CPLEX + CUTS

2007

"... In PAGE 126: ... CPLEX branch-and-bound was used to solve the two mixed integer programming formulations. Table1 1: Comparison of two formulations: lower and upper bounds were returned at the end of 300s of computation time (P1) (P2) Prob LB UB LB UB E10 10 0.00 0.... In PAGE 127: ... This shows that as an integer programming formulation, with no additional cuts or heuristics added, formulation (P 2) performs better than formulation (P 1). Table1 2: Comparison of two formulations: Node counts and solve times (P1) (P2) Prob Node Count Time Node Count Time E10 10 240 1.5 56 0.... In PAGE 128: ... The time limit was 300s, so if optimal solution is not found in the allotted time for a problem the corresponding entry for solve time is 300s and node count entry is the number of nodes explored in 300s. Table1 3: Comparison of two formulations with cutting planes and heuristics: lower and upper Bounds after 300s of computation time (P1) (P2) Prob LB UB LB UB E10 10 0.00 0.... In PAGE 128: ...00 0.00 Entries in bold represent that optimal solution was found in 300 second Looking at the results from Table1 3, we can see that, with the help of cuts and heuristics, formulation (P 1) was able to provide better results than (P 2). More problems were solved to optimality and for except one, the bounds provided for the problems not solved to optimality in allotted time by formulation (P 1) were stronger than formulation (P 2).... In PAGE 129: ...Table1 4: Comparison of two formulations with cutting Planes and heuristics: node counts and computation times (P1) (P2) Prob Node Count Time Node Count Time E10 10 0 4.01 0 0.... ..."

### Table 2: Linear model estimation

2006

"... In PAGE 9: ...Computational experience (MIPLIB instances) 3 OUR METHOD Table2 compares the size of the measurement tree obtained by the linear model with the actual number of nodes in T. The last column shows the ratio between the two.... ..."

Cited by 2

### TABLE I PROBLEM SPECIFICATIONS AND COMPUTATIONAL RESULTS Problem

### Table 2: Complexity of computation problems.

2007

### TABLE 2: The Inflation-Stabilization Problem in an Economy with Two Agents and Two Assets.

### Table 4. Computational performance on Problem 2: Computation of mixture critical points.

2002

"... In PAGE 21: ... For these problems, the effects of using both the new hybrid preconditioning strategy and the new real point selection scheme are considered. All the problems were successfully solved, with the computational performance shown in Table4 . Here the notation is the same as above, but with the additional notation HP/RP to indicate use of both the hybrid preconditioner and real point selection schemes.... ..."

Cited by 2

### Table 3. Value computation for sample problems.

1997

"... In PAGE 8: ... The tutor generates several problems and picks the one with the highest value. Table3 provides examples of problem goodness. The first problem matches for Simplify Fraction, Make Proper, and Equivalent Fractions, while the second problem only matches for Find LCM.... ..."

Cited by 23

### Table 3. Value computation for sample problems.

1997

"... In PAGE 8: ... The tutor generates several problems and picks the one with the highest value. Table3 provides examples of problem goodness. The first problem matches for Simplify Fraction, Make Proper, and Equivalent Fractions, while the second problem only matches for Find LCM.... ..."

Cited by 23

### Table 1 Problem dimensions and computing times

"... In PAGE 21: ...0 GB RAM. Table1 shows increased problem dimensions and computing times, for instances with 9, 11, and 31 selected scenarios. This shows, that ddsip finds solutions with reasonably small gaps in acceptable solution time for these large-scale stochastic mixed-integer linear programs.... ..."