Implicit filtering is a way to solve bound-constrained optimization problems for which derivative information is not available. Unlike methods that use interpolation to reconstruct the function and its higher derivatives, implicit filtering builds upon coordinate search and then interpolates to get an approximation of the gradient. Implicit Filtering describes the algorithm, its convergence theory and a new MATLAB(r) implementation. It is unique for being the only book in the area of derivative-free or sampling methods to be accompanied by publicly available software. It includes an overview of recent results on optimization of noisy functions, including results that depend on non-smooth analysis and results on the handling of constraints. This book is for graduate students who want to learn about this technology, scientists and engineers who wish to apply the methods to their problems and specialists who will use the ideas and the software from this book in their own research.