The field of computational complexity and optimization is moving towards more efficient and reliable methods for solving combinatorial optimization problems. Researchers are exploring alternative algorithmic techniques, such as pseudo-Boolean reasoning and stochastic local search, to improve the performance of existing frameworks. Additionally, there is a growing interest in investigating the complexity of functional synthesis problems in different theories, including Presburger Arithmetic. The recognition problem for certain graph classes, such as penny and marble graphs, has been shown to be computationally hard, and new notions of kernelization are being introduced to harness the power of SAT-solvers for preprocessing purposes. Noteworthy papers include: Efficient and Reliable Hitting-Set Computations for the Implicit Hitting Set Approach, which presents a trade-off between efficiency and reliability in hitting set optimization. Presburger Functional Synthesis: Complexity and Tractable Normal Forms, which investigates the complexity of functional synthesis in Presburger Arithmetic and identifies a special normal form for the specification formula that guarantees poly-time and poly-size solvability. Recognizing Penny and Marble Graphs is Hard for Existential Theory of the Reals, which shows that the recognition problem for penny and marble graphs is computationally hard. On Kernelization with Access to NP-Oracles, which introduces a new notion of kernelization that harnesses the power of SAT-solvers for preprocessing purposes.