Many problems of combinatorial optimization can be considered in a geometric context: vertices representing locations correspond to points, and edge weights arise from geometric cost. Moreover, geometric applications give rise to generalizations and variations: For example, if we need to cover a whole region instead of individual points, a Traveling Salesman Problem can turn into a Lawnmowing Problem. This makes is interesting to consider the interaction between discrete optimization and computational geometry.
In this talk, I will present a number of results for optimization problems for which geometric variants provide additional twists. Particular examples include touring, location and network problems:
As it turns out, these problems are not only of theoretical interest, but also relevant for a variety of applications.
Current 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996
