TAILIEUCHUNG - Handbook of algorithms for physical design automation part 66

Handbook of Algorithms for Physical Design Automation part 66 provides a detailed overview of VLSI physical design automation, emphasizing state-of-the-art techniques, trends and improvements that have emerged during the previous decade. After a brief introduction to the modern physical design problem, basic algorithmic techniques, and partitioning, the book discusses significant advances in floorplanning representations and describes recent formulations of the floorplanning problem. The text also addresses issues of placement, net layout and optimization, routing multiple signal nets, manufacturability, physical synthesis, special nets, and designing for specialized technologies. It includes a personal perspective from Ralph Otten as he looks back on. | 632 Handbook of Algorithms for Physical Design Automation SIMPLEX ALGORITHM WITH COLUMN GENERATION The first algorithm to solve the fractional global routing problem Equation by Hu and Shing in 1985 15 used the simplex algorithm by Dantzig in 1951 16 with column generation. This method finds an optimal solution even though it does not explicitly enumerate all the possible Steiner trees nor has variables for all Steiner trees in memory. The method is limited in the size of the problem instances and hence Hu and Shing propose a decomposition and cut-and-paste approach. In this section we show how the simplex algorithm with column generation is applied to the fractional global routing problem. The simplex method goes from vertex to vertex along edges of the polyhedron underlying the linear program until an optimal vertex is reached. For a complete description of the simplex method we refer the reader to Refs. 12 13 17 . Interesting is that this method requires a subroutine that computes minimal Steiner trees for nets with respect to a nonnegative length function on the edges a subroutine that is also required by the approximation schemes is presented later. The linear program of the fractional global routing problem Equation can be rewritten with matrices as follows in a standard form for linear programs min - M c 0 I 0 x k v 0 ix In this linear program the first constraint M -c l X 0 corresponds to the capacity v constraints on the edges the first inequality in Equation . Each row corresponds to an edge and each column of the matrix M corresponds to a Steiner tree T for a net i and is the incidence vector for the edges of the corresponding Steiner tree. The vector v contains slack variables for the equality constrains. x The second constraint N 0 0 X 1 of the linear program ensures that the weights xiT v for each net i and all Steiner trees T for the net sum up to 1 the second inequality in Equation . The matrix N has one row for each net

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