## 2013

# Interactive Tree Decomposition Tool for Reducing System Analysis Complexity

Yuchen Zhou, Shahan Yang and John Baras

*Proceedings of Conference on Systems Engineering Research (CSER’13), pp. 138-147, Atlanta, GA,* March 19-22, 2013.

**Abstract**

We present a graphical tool for the calculation of treewidth, a metric on the parametric structure of a system that is intimately tied
to the complexity of system analysis. For many graphically describable systems, such as systems of parametric equations, as in a
SysML Parametric diagram, or Bayesian networks or even mind maps and writing term papers, analysis of the system is
exponential in treewidth and linear in system size. A tool facilitating comprehensive analysis can serve to bring competitive
advantage to a systems engineering workflow by reducing costly unanticipated behaviors. Furthermore, a byproduct of
computing treewidth is a framework for enumerating computationally compatible distributed algorithms.
In this paper, we pose this NP-complete problem from the perspective of finding satisficing solutions, exposing choices that can
influence the complexity of the resulting system to the designer. A designer can contribute two important things to the structure
of the system: a visual intuition about the relationships between the underlying objects and the ability to change the relationships
themselves at design time to reduce analysis complexity. Having a visual tool that provides instant feedback will help designers
achieve an intuitive grasp of the relationship between design decisions and system complexity. As complexity is the root of
almost every systems engineering problem, and also something not easily understood, incorporating complexity analysis into a
design process should improve resulting system designs.

The tool uses a randomized, anytime algorithm for interactive optimization of treewidth. It presents a sequence of choices to a
designer and incrementally lowers an upper bound on system treewidth over time. This algorithm is novel, as few algorithms are
targeted at interactivity with a human user. We present a number of simple examples for using the tool. We show how our tool
helps to decompose some example systems, including a quadrotor optimization, a sensor network optimization, a Bayesian
network, and a mind map.