Abstract
We study three methods based on linear programming and generalizations that are often applied to approximate combinatorial optimization problems. We start by describing an approximate method based on linear programming; as an example we consider scheduling of jobs on unrelated machines with costs. The second method presented is based on semidefinite programming; we show how to obtain a reasonable solution for the maximum cut problem. Finally, we analyze the conditional probabilities method in connection with randomized rounding for routing, packing and covering integer linear programming problems.
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Jansen, K., Rolim, J. (1998). Algorithms Based on Randomization and Linear and Semidefinite Programming. In: Rovan, B. (eds) SOFSEM’ 98: Theory and Practice of Informatics. SOFSEM 1998. Lecture Notes in Computer Science, vol 1521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49477-4_8
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DOI: https://doi.org/10.1007/3-540-49477-4_8
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