In this paper, we propose a new coded computing technique called "substitute decoding" for general iterative distributed computation tasks. In the first part of the paper, we use PageRank as a simple example to show that substitute decoding can make the computation of power iterations solving PageRank on sparse matrices robust to erasures in distributed systems. For these sparse matrices, codes with dense generator matrices can significantly increase storage costs and codes with low-density generator matrices (LDGM) are preferred. Surprisingly, we show through both theoretical analysis and simulations that when substitute decoding is used, coded iterative computing with extremely low-density codes (2 or 3 non-zeros in each row of the generator matrix) can achieve almost the same convergence rate as noiseless techniques, despite the poor error-correction ability of LDGM codes. In the second part of the paper, we discuss applications of substitute decoding beyond solving linear systems and PageRank. These applications include (1) computing eigenvectors, (2) computing the truncated singular value decomposition (SVD), and (3) gradient descent. These examples show that the substitute decoding algorithm is useful in a wide range of applications.