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Although most tasks have a Due Date, it is not required. Tasks that do not have a Due Date are displayed in their own section, No Due Date, when tasks are organized by Due Date.

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subgraphs of the planar dual of the infinite triangular lattice Hexagonal grids form a regular arrangement of 6 cycles, and are bipartite Labelings for the hypercube Qn were described and analyzed in [15, 24, 41] Graph labelings for trees with constraints c1, c2 = 2, 1 were treated in [5] and [15] These labelings are obtained using a greedy approach, which is described in Section 534 532 Odd Cycles Channel assignment on odd cycles was first studied by Griggs and Yeh in [15] The authors give a graph labeling for constraints c1, c2 = 2, 1 of span 4 and cyclic span 6 The labeling repeats the channels 0, 2, 4 along the cycle, with a small adaptation near the end if the length of the cycle is not divisible by 3.





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Efficient solvers for sparse systems of linear algebraic equations are key ingredients of finite element programs. Nowadays an engineer or researcher hardly can afford developing matrix solvers on hisher own, and thus public domain software packages play an increasingly important role. Moreover, as the finite element software becomes more complex, the question of efficient simultaneous interfacing to multiple matrix solver packages matters. Every matrix solver comes with its own unique interface. Hardcoding this interface into a FEM solver means an unwanted coupling. If the FEM solver deals with multiple PDEs that produce matrices with substantially different properties (this is the case, e.g., with secondorder elliptic PDEs and Maxwell s equations), the application of multiple matrix solvers becomes a need.

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As described in the previous section, this labeling can be used for general constraints c1, c2 if all values assigned by the labeling are multiplied by max{c2, c1/2} Using Algorithm A , this leads to an algorithm with performance ratio max{1, 3c1/c0, 6c2/c0} In [21], three basic algorithms for odd cycles are combined in different ways to give optimal or near-optimal algorithms for all possible choices of two constraints c0 and c1 The first of the three algorithms in [21] is based on a graph labeling that satisfies one constraint c1 This labeling has cyclic span cR = 2nc1/(n 1) It starts by assigning zero to the first node, and then adding c1 (modulo cR) to the previously assigned channel and assigning this to the next node in the cycle At a certain point, this switches to an alternating assignment This labeling is then used repeatedly, as in Algorithm A .

Since this particular form of Algorithm A will be used to describe the further results in this chapter, I will state it explicitly below Algorithm C (for odd cycles) Let G = (V, E, c0, c1) be a constrained cycle of n nodes, where n > 3 is odd, and w be an arbitrary weight vector Fix s = max{c0, cR} Let the nodes of the cycle be numbered {1, , n}, numbered in cyclic order, where node 1 is a node of maximum weight in the cycle Let m > 1 be the smallest odd integer such that s 2m/(m 1)c1 (it can be shown that such an integer must exist) ASSIGNMENT: To each node i, the algorithm assigns the channels b(i) + js, where j = 0, .

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Another way to execute blocks of code is the subroutine. The analogous idea for a group of subroutines is called a module, but this topic is more advanced: see section 9.2. The default in Per1 allows subroutines to access and modify any variables in the main code. This can cause problems, especially if subroutines are reused in other programs. The solution is easy: make the variables in a subroutine local to that subroutine, which can be done with the my statement at the beginning for all the variables at once, as shown below.

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