The discipline of mixed-integer nonlinear programming (MINLP) deals with finite-dimensional optimization problems featuring both discrete choices and nonlinear functions. By this combination, it facilitates more accurate models of real-world systems than possible with purely continuous or purely linear models alone. This book presents new methods that improve the numerical reliability and the computational performance of global MINLP solvers. The author addresses numerical accuracy directly at the linear programming level by means of LP iterative refinement: a new algorithm to solve linear programs to arbitrarily high levels of precision. The computational performance of LP-based MINLP solvers is enhanced by efficient methods to execute and approximate optimization-based bound tightening and by new branching rules that exploit the presence of nonlinear integer variables, i.e., variables both contained in nonlinear terms and required to be integral. The new algorithms help to solve problems which could not be solved before, either due to their numerical complexity or because of limited computing resources.