We present a prescription for performing electronic-structure calculations without the explicit use of a basis. Our prescription combines a higher-order finite-difference method with ab initio pseudopotentials. In contrast to methods that combine a plane-wave basis with pseudopotentials, our calculations are performed completely in real space. No artifacts such as supercell geometries need be introduced for localized systems. Although this approach is easier to implement than one that employs a plane-wave basis, no loss of accuracy occurs. We apply this method to calculate the structural and electronic properties of several diatomic molecules: ${\mathrm{Si}}_2$, ${\mathrm{C}}_2$, ${\mathrm{O}}_2$, and CO.

In this paper, we propose a restarted variant of the Lanczos method for symmetric eigenvalue problems named the thick-restart Lanczos method. This new variant is able to retain an arbitrary number of Ritz vectors from the previous iterations with a minimal restarting cost. Since it restarts with Ritz vectors, it is simpler than similar methods, such as the implicitly restarted Lanczos method. We carefully examine the effects of the floating-point round-off errors on stability of the new algorithm and present an implementation of the partial reorthogonalization scheme that guarantees accurate Ritz values with a minimal amount of reorthogonalization. We also show a number of heuristics on deciding which Ritz pairs to save during restart in order to maximize the overall performance of the thick-restart Lanczos method.

We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.

Bitmap indices are efficient for answering queries on low-cardinality attributes. In this article, we present a new compression scheme called Word-Aligned Hybrid (WAH) code that makes compressed bitmap indices efficient even for high-cardinality attributes. We further prove that the new compressed bitmap index, like the best variants of the B-tree index, is optimal for one-dimensional range queries. More specifically, the time required to answer a one-dimensional range query is a linear function of the number of hits. This strongly supports the well-known observation that compressed bitmap indices are efficient for multidimensional range queries because results of one-dimensional range queries computed with bitmap indices can be easily combined to answer multidimensional range queries. Our timing measurements on range queries not only confirm the linear relationship between the query response time and the number of hits, but also demonstrate that WAH compressed indices answer queries faster than the commonly used indices including projection indices, B-tree indices, and other compressed bitmap indices.