I have the following sparse matrix, which relates to a markov process. The parts of the matrix have been assembled sequentially, adding new entries to row, column, and probability one at a time, and only then creating
S = sparse(row,column,probability)
Because the sequential process involves aggregating probabilities from some states that are equaivalent
full(S)
results in a matrix, whose rows sum to more than one. What I wish to achieve is a normalization of each row in S, such that all rows sum to one. How can that be done by operating on S without needing to create the full matrix?
Best Answer