Empirical algorithmics

Abstract: Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation.

Abstract: Recent demonstrations of superconducting quantum computers by Google and IBM and trapped-ion computers from IonQ fueled new research in quantum algorithms, compilation into quantum circuits, and empirical algorithmics. While online access to quantum hardware remains too limited to meet the demand, simulating quantum circuits on conventional computers satisfies many needs. We advance Schrodinger-style simulation of quantum circuits that is useful standalone and as a building block in layered simulation algorithms, both cases are illustrated in our results. Our algorithmic contributions show how to simulate multiple quantum gates at once, how to avoid floating-point multiplies, how to best use data-level and thread-level parallelism as well as CPU cache, and how to leverage these optimizations by reordering circuit gates. While not described previously, these techniques implemented by us supported published high-performance distributed simulations up to 64 qubits. To show additional impact, we benchmark our simulator against Microsoft, IBM and Google simulators on hard circuits from Google.

Abstract: Algorithm selection (AS) techniques – which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently – have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. In this extended abstract of our 2015 JAIR article of the same title, we summarize AUTOFOLIO, which uses an algorithm configuration procedure to automatically select an AS approach and optimize its parameters for a given AS scenario. AUTOFOLIO allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods and to automatically construct high-performance algorithm selectors. We demonstrate that AUTOFOLIO was able to produce new state-of-the-art algorithm selectors for 7 well-studied AS scenarios and matches state-ofthe-art performance statistically on all other scenarios. Compared to the best single algorithm for each AS scenario, AUTOFOLIO achieved average speedup factors between 1.3 and 15.4.

Abstract: Computer science is often divided into two camps, systems and theory, but of course the reality is more complicated and more interesting than that. One example is the area of "experimental algorithmics," also termed "empirical algorithmics." This fascinating discipline marries algorithm analysis, which is often done with mathematical proofs, with experimentation with real programs running on real machines.