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The SoR (Sum-of-Ratios) problem intended to minimize (maximize) a sum of several fractional functions in convex set is a non-convex optimization problem that is difficult to solve by traditional optimization methods. The CMP (Convex Multiplicative Programming) problem is to minimize the sum of products of two convex functions in convex set. The SoR and CMP problems arise in many applications such as the communication, robotics, computer graphics, finance, engineering, plant layout design, robust optimization, VLSI chip design, data mining and so on.This book presents new parametric approach to the SoR and CMP problem. Compared with existing methods based on branch-and-bound procedure and other approaches, the idea of new method is to reduce the SoR and CMP problems to parametric convex programming problem having parameters in objective functions. The parametric algorithm is based on Newton-like method for solving a system of nonlinear equations with parameters and it needs to solve convex programming problem in each iteration. This new algorithm has the global linear and local superlinear/quadratic rate of convergence.