Resolution Trees with Lemmas: Resolution Refinements that Characterize DLL Algorithms with Clause Learning

Resolution refinements called w-resolution trees with lemmas (WRTL) and with input lemmas (WRTI) are introduced. Dag-like resolution is equivalent to both WRTL and WRTI when there is no regularity condition. For regular proofs, an exponential separation between regular dag-like resolution and both regular WRTL and regular WRTI is given. It is proved that DLL proof search algorithms that use clause learning based on unit propagation can be polynomially simulated by regular WRTI. More generally, non-greedy DLL algorithms with learning by unit propagation are equivalent to regular WRTI. A general form of clause learning, called DLL-Learn, is defined that is equivalent to regular WRTL. A variable extension method is used to give simulations of resolution by regular WRTI, using a simplified form of proof trace extensions. DLL-Learn and non-greedy DLL algorithms with learning by unit propagation can use variable extensions to simulate general resolution without doing restarts. Finally, an exponential lower bound for WRTL where the lemmas are restricted to short clauses is shown

Types with potential: polynomial resource bounds via automatic amortized analysis

A primary feature of a computer program is its quantitative performance characteristics: the amount of resources such as time, memory, and power the program needs to perform its task. Concrete resource bounds for specific hardware have many important applications in software development but their manual determination is tedious and error-prone. This dissertation studies the problem of automatically determining concrete worst-case bounds on the quantitative resource consumption of functional programs. Traditionally, automatic resource analyses are based on recurrence relations. The difficulty of both extracting and solving recurrence relations has led to the development of type-based resource analyses that are compositional, modular, and formally verifiable. However, existing automatic analyses based on amortization or sized types can only compute bounds that are linear in the sizes of the arguments of a function. This work presents a novel type system that derives polynomial bounds from first-order functional programs. As pioneered by Hofmann and Jost for linear bounds, it relies on the potential method of amortized analysis. Types are annotated with multivariate resource polynomials, a rich class of functions that generalize non-negative linear combinations of binomial coefficients. The main theorem states that type derivations establish resource bounds that are sound with respect to the resource-consumption of programs which is formalized by a big-step operational semantics. Simple local type rules allow for an efficient inference algorithm for the type annotations which relies on linear constraint solving only. This gives rise to an analysis system that is fully automatic if a maximal degree of the bounding polynomials is given. The analysis is generic in the resource of interest and can derive bounds on time and space usage. The bounds are naturally closed under composition and eventually summarized in closed, easily understood formulas. The practicability of this automatic amortized analysis is verified with a publicly available implementation and a reproducible experimental evaluation. The experiments with a wide range of examples from functional programming show that the inference of the bounds only takes a couple of seconds in most cases. The derived heap-space and evaluation-step bounds are compared with the measured worst-case behavior of the programs. Most bounds are asymptotically tight, and the constant factors are close or even identical to the optimal ones. For the first time we are able to automatically and precisely analyze the resource consumption of involved programs such as quick sort for lists of lists, longest common subsequence via dynamic programming, and multiplication of a list of matrices with different, fitting dimensions

Arrays and References in Resource Aware ML

This article introduces a technique to accurately perform static prediction of resource usage for ML-like functional programs with references and arrays. Previous research successfully integrated the potential method of amortized analysis with a standard type system to automatically derive parametric resource bounds. The analysis is naturally compositional and the resource consumption of functions can be abstracted using potential-annotated types. The soundness theorem of the analysis guarantees that the derived bounds are correct with respect to the resource usage defined by a cost semantics. Type inference can be efficiently automated using off-the-shelf LP solvers, even if the derived bounds are polynomials. However, side effects and aliasing of heap references make it notoriously difficult to derive bounds that depend on mutable structures, such as arrays and references. As a result, existing automatic amortized analysis systems for ML-like programs cannot derive bounds for programs whose resource consumption depends on data in such structures. This article extends the potential method to handle mutable structures with minimal changes to the type rules while preserving the stated advantages of amortized analysis. To do so, we introduce a swap operation for references and arrays that users can use to make programs suitable for automatic analysis. We prove the soundness of the analysis introducing a potential-annotated memory typing, which gathers all unique locations reachable from a reference. Apart from the design of the system, the main contribution is the proof of soundness for the extended analysis system