Exploiting Sparsity in Jacobian Computation via Coloring and Automatic Differentiation: A Case Study in a Simulated Moving Bed Process

Using a model from a chromatographic separation process in chemical engineering, we demonstrate that large, sparse Jacobians of fairly complex structures can be computed accurately and efficiently by using automatic differentiation (AD) in combination with a four-step procedure involving matrix compression and de-compression. For the detection of sparsity pattern (step 1), we employ a new operator overloading-based implementation of a technique that relies on propagation of index domains. To obtain the seed matrix to be used for compression (step 2), we use a distance-2 coloring of the bipartite graph representation of the Jacobian. The compressed Jacobian is computed using the vector forward mode of AD (step 3). A simple routine is used to directly recover the entries of the Jacobian from the compressed representation (step 4). Experimental results using ADOL-C show that the runtimes of each of these steps is in complete agreement with theoretical analysis, and the total runtime is found to be only about a hundred times the time needed for evaluating the function itself. The alternative approach of computing the Jacobian without exploiting sparsity is infeasible.