Seminars in June 2012

  • Sang June Lee (이상준), Dynamic coloring and list dynamic coloring of planar graphs

    Dynamic coloring and list dynamic coloring of planar graphs
    Sang June Lee (이상준)
    Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia, USA
    2012/06/27 Wed 4PM-5PM

    A dynamic coloring of a graph G is a proper coloring of the vertex set V(G) such that for each vertex of degree at least 2, its neighbors receive at least two distinct colors. A dynamic k-coloring of a graph is a dynamic coloring with k colors. Note that the gap χd(G) – χ(G) could be arbitrarily large for some graphs. An interesting problem is to study which graphs have small values of χd(G) – χ(G).
    One of the most interesting problems about dynamic chromatic numbers is to find upper bounds of χd(G)$ for planar graphs G. Lin and Zhao (2010) and Fan, Lai, and Chen (recently) showed that for every planar graph G, we have χd(G)≤5, and it was conjectured that χd(G)≤4 if G is a planar graph other than C5. (Note that χd(C5)=5.)
    As a partial answer, Meng, Miao, Su, and Li (2006) showed that the dynamic chromatic number of Pseudo-Halin graphs, which are planar graphs, are at most 4, and Kim and Park (2011) showed that χd(G)≤4 if G is a planar graph with girth at least 7.
    In this talk we settle the above conjecture that χd≤4 if G is a planar graph other than C5. We also study the corresponding list coloring called a list dynamic coloring.
    This is joint work with Seog-Jin Kim and Won-Jin Park.

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  • FYI: George L. Nemhauser, Branch-and-price Guided Search

    FYI (ISyE Special Seminar)
    Branch-and-Price Guided Search
    George L. Nemhauser
    School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
    2012/6/20 Wed 4PM-5PM (Building E2-2, Room 1501)
    We present an approach for structured integer programs that solves well-chosen restrictions of the problem to produce high-quality solutions quickly. Column generation is used both for automatically generating the restrictions and for producing bounds on the value of an optimal solution. We present computational experience for fixed-charge multi-commodity flow and inventory routing problems.

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