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We consider the problem of maximizing submodular functions under submodular constraints by formulating the problem in two ways: \SCSKC and \DiffC. Given two submodular functions $$f$$ and $$g$$ where $$f$$ is monotone, the objective of \SCSKC problem is to find a set $$S$$ of size at most $$k$$ that maximizes $f(S)$ under the constraint that $$g(S)\leq \theta$$, for a given value of $$\theta$$. The problem of \DiffC focuses on finding a set $$S$$ of size at most $$k$$ such that $h(S) = f(S)-g(S)$$ is maximized. It is known that these problems are highly inapproximable and do not admit any constant factor multiplicative approximation algorithms unless NP is easy. Known approximation algorithms involve data-dependent approximation factors that are not efficiently computable. We initiate a study of the design of approximation algorithms where the approximation factors are efficiently computable. For the problem of \SCSKC, we prove that the greedy algorithm produces a solution whose value is at least $$(1-1/e)f(\OPT) - A$, where $$A$$ is the data-dependent additive error. For the \DiffC problem, we design an algorithm that uses the \SCSKC greedy algorithm as a subroutine. This algorithm produces a solution whose value is at least $$(1-1/e)h(\OPT)-B$, where $$B$$ is also a data-dependent additive error. A salient feature of our approach is that the additive error terms can be computed efficiently, thus enabling us to ascertain the quality of the solutions produced.