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The maximum ratio clique problem

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Abstract

This paper introduces a fractional version of the classical maximum weight clique problem, the maximum ratio clique problem, which is to find a maximal clique that has the largest ratio of benefit and cost weights associated with the clique’s vertices. NP-completeness of the decision version of the problem is established, and three solution methods are proposed. The results of numerical experiments with standard graph instances, as well as with real-life instances arising in finance and energy systems, are reported.

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References

  • Billionnet A (2002) Approximation algorithms for fractional knapsack problems. Oper Res Lett 30(5): 336–342

    Google Scholar 

  • Boginski V, Butenko S, Pardalos P (2006) Mining market data: a network approach. Comput Oper Res 33:3171–3184

    Article  Google Scholar 

  • Bomze IM, Budinich M, Pardalos PM, Pelillo M (1999) The maximum clique problem. In: Du DZ, Pardalos PM (eds) Handbook of combinatorial optimization. Kluwer Academic Publishers, Dordrecht, pp 1–74

    Chapter  Google Scholar 

  • Chandrasekaran R (1977) Minimal ratio spanning trees. Networks 7(4):335–342

    Article  Google Scholar 

  • Chvátal V, Slater P (1993) A note on well-covered graphs. Ann Discrete Math 55:179–182

    Article  Google Scholar 

  • Dantzig GB, Blattner WO, Rao MR (1967) Finding a cycle in a graph with minimum cost to time ratio with application to a ship routing problem. In: Rosentlehl P (ed) Theory of graphs. Gordon and Breach, New York, pp 77–84

    Google Scholar 

  • Dasdan A, Gupta R (1998) Faster maximum and minimum mean cycle algorithms for system-performance analysis. IEEE Trans Comput Aided Des Integr Circ Syst 17(10):889–899

    Article  Google Scholar 

  • Dinkelbach W (1967) On nonlinear fractional programming. Manag Sci 13(7):492–498

    Article  Google Scholar 

  • Elhedhli S (2005) Exact solution of a class of nonlinear knapsack problems. Oper Res Lett 33:615–624

    Article  Google Scholar 

  • Fox B (1969) Finding minimal cost-time ratio circuits. Oper Res 17(3):546–551

    Article  Google Scholar 

  • Garey MR, Johnson DS (1979) Computers and Intractability: a Guide to the theory of NP-completeness. W.H. Freeman and Company, New York

    Google Scholar 

  • Ibaraki T (1983) Parametric approaches to fractional programs. Math Program 26:345–362

    Article  Google Scholar 

  • Isbell JR, Marlow WH (1956) Attrition games. Naval Res Logist Q 3(1–2):71–94

    Article  Google Scholar 

  • Karp RM (1978) A characterization of the minimum cycle mean in a digraph. Discrete Math 23(3):309–311

    Article  Google Scholar 

  • Lawler EL (1976) Combinatorial optimization: networks and matroids. Holt, Rinehart and Winston, New York

  • Luce R, Perry A (1949) A method of matrix analysis of group structure. Psychometrika 14:95–116

    Article  Google Scholar 

  • Megiddo N (1979) Combinatorial optimization with rational objective functions. Math Oper Res 4(4):414–424

    Article  Google Scholar 

  • NREL: transmission grid integration - data and resources (2012). http://www.nrel.gov/electricity/transmission/data_resources.html. Accessed October 2013

  • Orlin JB, Ahuja RK (1992) New scaling algorithms for the assignment and minimum mean cycle problems. Math Program 54:41–56

    Article  Google Scholar 

  • Picard JC, Queyranne M (1982) A network flow solution to some nonlinear 0–1 programming problems, with applications to graph theory. Networks 12:141–159

    Article  Google Scholar 

  • Prokopyev O, Meneses C, Oliveira C, Pardalos P (2005) On multiple-ratio hyperbolic 0–1 programming problems. Pac J Optim 1(2):327–345

    Google Scholar 

  • Prokopyev OA, Huang H, Pardalos PM (2005) On complexity of unconstrained hyperbolic 0–1 programming problems. Oper Res Lett 33(3):312–318

    Article  Google Scholar 

  • Radzik T (1998) Fractional combinatorial optimization. In: Du DZ, Pardalos P (eds) Handbook of combinatorial optimization, vol 1. Kluwer Academic Publishers, pp 429–478

  • Radzik T (1992) Newton’s method for fractional combinatorial optimization. In: Proceedings of 33rd annual symposium on foundations of computer science, pp 659–669

  • Sankaranarayana R, Stewart L (1992) Complexity results for well-covered graphs. Networks 22:247–262

    Article  Google Scholar 

  • Shigeno M, Saruwatari Y, Matsui T (1995) An algorithm for fractional assignment problems. Discrete Appl Math 56:333–343

    Article  Google Scholar 

  • Skiscim C, Palocsay S (2004) The complexity of minimum ratio spanning tree problems. J Glob Optim 30:335–346

    Article  Google Scholar 

  • Tawarmalani M, Ahmed S, Sahinidis NV (2002) Global optimization of 0–1 hyperbolic programs. J Glob Optim 24:385–416

    Article  Google Scholar 

  • Ursulenko O, Butenko S, Prokopyev O (2013) A global optimization algorithm for solving the minimum multiple ratio spanning tree problem. J Glob Optim 56:1029–1043

    Article  Google Scholar 

  • Wiser R, Lantz E, Bolinger M, Hand M (2012) Recent developments in the levelized cost of energy from us wind power projects. http://eetd.lbl.gov/ea/emp/reports/wind-energy-costs-2-2012.pdf. Accessed October 2013

  • Wu T (1997) A note on a global approach for general 0–1 fractional programming. Eur J Oper Res 101(1):220–223

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by the US Department of Energy Grant DE-SC0002051 and US Air Force Office of Scientific Research Award No. FA9550-12-1-0103. The authors gratefully acknowledge the comments by two referees.

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Correspondence to Samyukta Sethuraman.

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Sethuraman, S., Butenko, S. The maximum ratio clique problem. Comput Manag Sci 12, 197–218 (2015). https://doi.org/10.1007/s10287-013-0197-z

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