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H.A. Almohamad, S.O. Duffuaa, "A Linear Programming Approach for the Weighted Graph Matching Problem," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 5, pp. 522525, May, 1993.  
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@article{ 10.1109/34.211474, author = {H.A. Almohamad and S.O. Duffuaa}, title = {A Linear Programming Approach for the Weighted Graph Matching Problem}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {15}, number = {5}, issn = {01628828}, year = {1993}, pages = {522525}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.211474}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Linear Programming Approach for the Weighted Graph Matching Problem IS  5 SN  01628828 SP522 EP525 EPD  522525 A1  H.A. Almohamad, A1  S.O. Duffuaa, PY  1993 KW  computational complexity; linear programming; weighted graph matching; quadratic optimization; simplexbased algorithm; Hungarian method; polynomial time; eigendecomposition; symmetric polynomial transform; computational complexity; linear programming; pattern recognition VL  15 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A linear programming (LP) approach is proposed for the weighted graph matching problem. A linear program is obtained by formulating the graph matching problem in L/sub 1/ norm and then transforming the resulting quadratic optimization problem to a linear one. The linear program is solved using a simplexbased algorithm. Then, approximate 01 integer solutions are obtained by applying the Hungarian method on the real solutions of the linear program. The complexity of the proposed algorithm is polynomial time, and it is O(n/sup 6/L) for matching graphs of size n. The developed algorithm is compared to two other algorithms. One is based on an eigendecomposition approach and the other on a symmetric polynomial transform. Experimental results showed that the LP approach is superior in matching graphs than both other methods.
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