### TABLE III ALGORITHM FOR MAXIMUM CONCURRENT FLOW PROBLEM

2004

Cited by 6

### Table 1. List of applications of ACO algorithms to static combinatorial optimization problems. Classification by application and chronologically ordered.

1999

Cited by 209

### Table 2. List of applications of ACO algorithms to dynamic combinatorial optimization problems. Classification by application and chronologically ordered.

1999

Cited by 209

### Table 4: Energy costs of the LP-based (1 + )- approximation, the combinatorial algorithm as de- scribed, and the combinatorial algorithm using a - good set of guards.

2005

"... In PAGE 10: ... In a last experiment we have run the simple combinatorial algorithm on a -good set of sample points. Even though we cannot prove any better approximation ratio than for the original algorithm, the results look quite promising as can be seen in Table4 . Here we denote by C1+ simple the outcome of running the O(1) algorithm on an -good set of guards, including the required power-up.... ..."

Cited by 1

### Table 2.1. List of applications of ACO algorithms to combinatorial optimization problems. Classi cation by application and chronologically ordered.

### Table 1 Combinatorial optimization problems and their geometric equivalents

"... In PAGE 10: ... Similarly, if we color the graph with a minimum number of colors, then this is equivalent with dividing the collection of lines into a minimum number of subcollections such that each subcollection contains no parallel pairs of lines. Table1 gives an overview of problems in graph theory and their geometric equivalent. Since in this paper we focus on parallel line grouping, we propose two combi- natorial algorithms that can be used to partition a graph of parallel pairs into subgraphs which are or which resemble cliques.... ..."

### Table 2 shows how the axioms for the Saga trans-

1993

Cited by 136

### Table 1: The rst column under each heading summarizes the results in this paper except hierarchical clustering; (cons) denotes result for constrained problems, (unc) unconstrained problems. The next two columns give results for unconstrained problems, given by randomized pivoting (ACN) and randomized LP rounding (ACN-LP). The result in column (CFR) is obtained by a deterministic combinatorial algorithm. The approximation guarantees for our combinatorial algorithm for unconstrained and constrained ranking with probability constraints are given in parentheses. The last row gives the results when taking the best of the algorithm generated solution and a random input permutation/clustering.

### Table 1: Algorithm for the Maximum Flow Problem

2004

"... In PAGE 4: ...Table 1: Algorithm for the Maximum Flow Problem The algorithm for the maximum flow problem, henceforth re- ferred to as MaxFlow, is shown in Table1 . Initially, we set de = fl for each edge e 2 E, and fi j = 0 for each tree ti j in each ses- sion Si.... In PAGE 8: ... Consequently, the previous studied problems M1, M2, M2I, as well as their duals, have to be reformulated to ac- commodate such a redefinition. Note that the essence of algorithms ( Table1 , 3, 5 and 6) to all these problems is to assign length de to each physical edge e 2 E, such that the length of any overlay span-... ..."

Cited by 6

### Table 1. Some of the current applications of ACO algorithms. Applications are listed by class of problems and in chronological order.

2001

Cited by 2