Constrained stress majorization is a promising new technique for integrating application specific layout constraints into forcedirected graph layout. We significantly improve the speed and convergence properties of the constrained stress-majorization technique for graph layout by employing a diagonal scaling of the stress function. Diagonal scaling requires the active-set quadratic programming ...

Defender-Attacker Stackelberg games are the foundations of tools deployed for computing optimal patrolling strategies in adversarial domains such as the United states Federal Air Marshals Service and the United States Coast Guard, among others. In Stackelberg game models of these systems the attacker knows only the probability that each target is covered by the defender, but is oblivious to the...

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Degree-bounded spanning problems are well known and are mainly used to solve capacity constrained communication (routing) problems. The degree-constrained spanning tree problems are NP-hard and the minimum cost spanning tree is not approximable nor in the case where the entire graph must be spanned neither in partial spanning problems. Most of applications (such as communications) do not need t...

The most commonly used method to tackle the graph partitioning problem in practice is the multilevel approach. During a coarsening phase, a multilevel graph partitioning algorithm reduces the graph size by iteratively contracting nodes and edges until the graph is small enough to be partitioned by some other algorithm. A partition of the input graph is then constructed by successively transferr...

Finding the degree-constrained minimum spanning tree (d-MST) of a graph is a wellstudied NP-hard problem of importance in communications network design and other network-related problems. In this paper we describe some previously proposed algorithms for solving the problem, and then introduce a novel tree construction algorithm called the Randomised Primal Method (RPM) which builds degree-const...

Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or constraint satisfaction problems. Constrained satisfaction problems (CSP) have taken an important attention from the research community due to their applicability to real problems. ...