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Graphs, Algorithms, and Optimization Donald L. Kreher, William Kocay
Publisher: Chapman and Hall/CRC

(An example of something that is not helpful I'd be surprised if the bottleneck weren't elsewhere. Many of the striking advances in theoretical computer science over the past two decades concern approximation algorithms, which compute provably near-optimal solutions to NP-hard optimization problems. N3, n1, n5], n5: [n5], n1: [n1, n2, n3, n5]} . Default speed should be the good one. The shape of the graph is between Früchterman & Rheingold's graph (scaling, gravity…). Is a continuous algorithm, that allows you to manipulate the graph while it is rendering (a classic force-vector, like Fruchterman Rheingold, and unlike OpenOrd); Has a linear-linear model (attraction and repulsion proportional to distance between nodes). Genetic algorithm produces a lot of the same results with the same optimized parameters' values. You can see it on the right part of your picture. The way to do this search for all possible words is by viewing the letters as a directed graph where the letters are nodes and edges are connections between adjacent letters. @Jason: If you want to optimize that algorithm for speed, put the mark bit in the vertex itself rather than looking it up in an external visited set. I'm floundering with finding graph algorithm references online, so if anyone could point me at an efficient algorithm description for reachability, I'd appreciate it. Yet the approximability of several fundamental problems such as TSP, Graph Coloring, Graph Partitioning etc. Keywords: Gate-level area optimization, multiple constant multiplications, Common Sub-expression Elimination (CSE) algorithm, Graph Base (GB) algorithm.