The NP-complete k-SAT problem - decide wether a given formula is satisfiable - is of fundamental importance in theoretical computer science. In this dissertation we study random 4-SAT formulas with > 116 n^2 clauses. These formulas are almost surly unsatisfiable. Here we show the existence of a polynomial time algorithm that certifies the unsatisfiability. Therefore we study the discrepancy of hypergraphs and multigraphs. We also combine spectral techniques with approximation algorithms to achieve the new result. Our new algorithm is adaptable for Not-All-Equal-4-SAT and the 2-colouring of 4-uniform hypergraphs. We also extends the Hajos construction of non k-colourable graphs to non k-colourable uniform hypergraphs. / Das NP-vollständige Problem k-SAT ist von zentraler Bedeutung in der theoretischen Informatik. In der Dissertation werden zufällige 4-SAT-Formeln mit > n^2 vielen Klauseln studiert. Diese Formeln sind mit hoher Wahrscheinlichkeit unerfüllbar. Hier wird erstmalig die Existenz eines Algorithmus gezeigt, der diese Unerfüllbarkeit effizient verifiziert. Hierfür wird die geringe Diskrepanz von Hypergrpahen und Multigraphen betrachtet. Der Schlüssel zu diesem Algorithmus liegt in der Kombination von spektralen Techniken mit Approximationsalgorithmen der klassischen kombinatorischen Optimierung. Der vorgestellte Algorithmus kann auf den effizienten Nachweis der Unerfüllbarkeit von Not-All-Equal-4-SAT-Formeln und die Nicht-2-Färbbarkeit von 4-uniformen Hypergraphen erweitert werden. Es wird ebenfalls eine Erweiterung der Hajos-Konstruktion nicht k-färbbarer Graphen auf nicht k-färbbare uniforme Hypergraphen angegeben.

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology.

Model predictive control is a popular research focus in electric motor control as it allows designers to specify optimization goals and exhibits fast transient response. Availability of faster and more affordable computers makes it possible to implement these algorithms in real-time. Real-time implementation is not without challenges however as these algorithms exhibit high computational complexity. Field-programmable gate arrays are a potential solution to the high computational requirements. However, they can be time-consuming to develop for. In this thesis, we present a methodology that reduces the size and development time of field-programmable gate array based fixed-point model predictive motor controllers using automated numerical analysis, optimization and code generation. The methods can be applied to other domains where model predictive control is used. Here, we demonstrate the benefits of our methodology by using it to build a motor controller at various sampling rates for an interior permanent magnet synchronous motor, tested in simulation at up to 125 kHz. Performance is then evaluated on a physical test bench with sampling rates up to 35 kHz, limited by the inverter. Our results show that the low latency achievable in our design allows for the exclusion of delay compensation common in other implementations and that automated reduction of numerical precision can allow the controller design to be compacted. / Thesis / Master of Applied Science (MASc)