The polyhedral model provides a highly expressive intermediate representation which is convenient for the analysis and subsequent transformation of affine loop nests. Several heuristics exist for achieving complex program transformations in this model. However, there is also considerable scope to utilize this model to tackle the problem of automatic memory footprint optimization. In this paper, we present a new automatic storage optimization technique which can be used to achieve both intra-array as well as inter-array storage reuse with a pre-determined schedule. Our approach works by finding storage partitioning hyperplanes for statements which partition a unified global array space so that values with overlapping live ranges are not mapped to the same partition. Our heuristic is driven by a four-fold objective function which not only minimizes the dimensionality and storage requirements of arrays required for each high-level statement, but also maximizes inter-statement storage reuse. The storage mappings obtained using our heuristic can be asymptotically better than those obtained by any existing technique. We implement our technique and demonstrate its practical impact by evaluating its effectiveness on several real-world examples chosen from the domains of image processing, stencil computations, and high-performance computing.