Propositional Resolution and SAT Solvers

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Page 3 of 3

Explores Quantum Approximate Optimization Algorithm for solving combinatorial problems efficiently.

Covers the interpretation of propositional logic and inference rules for implication, conjunction, and double negation.

Introduces Satisfiability Modulo Theories (SMT) solving, covering propositional logic, uninterpreted functions, and quantifier instantiation.

Covers the representation of mathematical models, functions, and data structures in information systems.

Introduces differential forms on manifolds, covering tangent bundles and intersection pairings.

Covers satisfiability threshold, clusters of solutions, parameter alpha, and average cluster computation.

Covers advanced Python programming concepts, including data structures and functions.

Covers the basics of C++ standard library containers, algorithms, and iterators.

Discusses data abstraction in programming, focusing on modules and specifications in Coq.