论文信息 - An approximation algorithm for the maximum cut problem and its experimental analysis

An approximation algorithm for the maximum cut problem and its experimental analysis

Abstract An approximation algorithm for the maximum cut problem is designed and analyzed; its performance is experimentally compared with that of a neural algorithm and that of Goemans and Williamson's algorithm. Although the guaranteed quality of our algorithm in the worst-case analysis is poor, we give experimental evidence that its average behavior is better than that of Goemans and Williamson's algorithm.

[1] David P. Williamson,et al. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming , 1995, JACM.

[2] Johan Håstad,et al. Some optimal inapproximability results , 2001, JACM.

[3] Satissed Now Consider. Improved Approximation Algorithms for Maximum Cut and Satissability Problems Using Semideenite Programming , 1997 .

[4] Robert J. Vanderbei,et al. An Interior-Point Method for Semidefinite Programming , 1996, SIAM J. Optim..

[5] Panos M. Pardalos,et al. Advances in Optimization and Parallel Computing , 1992 .

[6] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[7] Siam J. CoMPtrr,et al. FINDING A MAXIMUM CUT OF A PLANAR GRAPH IN POLYNOMIAL TIME * , 2022 .

[8] L. Devroye. Non-Uniform Random Variate Generation , 1986 .

[9] Paola Campadelli,et al. A Neural Algorithm for MAX-2SAT: Performance Analysis and Circuit Implementation , 1997, Neural Networks.