Positive-instance driven dynamic programming for treewidth

Positive-Instance Driven Dynamic Programming for Treewidth

Consider a dynamic programming scheme for a decision problem in which all subproblems involved are also decision problems. An implementation of such a scheme is positive-instance driven (PID), if it generates positive subproblem instances, but not negative ones, building each on smaller positive instances. We take the dynamic programming scheme due to Bouchitté and Todinca for treewidth computa...

Dynamic Programming on Graphs with Bounded Treewidth

Robust Data-Driven Dynamic Programming

In stochastic optimal control the distribution of the exogenous noise is typically unknown and must be inferred from limited data before dynamic programming (DP)-based solution schemes can be applied. If the conditional expectations in the DP recursions are estimated via kernel regression, however, the historical sample paths enter the solution procedure directly as they determine the evaluatio...

Dynamic Algorithms for Graphs with Treewidth 2

In this paper, we consider algorithms for maintaining tree-decompositions with constant bounded treewidth under edge and vertex insertions and deletions for graphs with treewidth at most 2 (also called: partial 2-trees, or series-parallel graphs), and for almost trees with parameter k. Each operation can be performed in O(log n) time. For a large number of graph decision, optimization and count...

Target Driven Instance Detection

While state-of-the-art general object detectors are getting better and better, there are not many systems specifically designed to take advantage of the instance detection problem. For many applications, such as household robotics, a system may need to recognize a few very specific instances at a time. Speed can be critical in these applications, as can the need to recognize previously unseen i...