|dc.description.abstract||Many machine learning and signal processing problems are fundamentally nonconvex. One way to solve them is to transform them into convex optimization problems (a.k.a. convex relaxation), which constitutes a major part of my research. Although the convex relaxation approach is elegant in some ways that it can give information-theoretical sample convexity and minimax denoising rate, but this approach is not efficient in dealing with high-dimensional problems. Therefore, as my second major part of the research, I will directly focus on the fundamentally nonconvex formulations of these nonconvex problems, with a particular interest in understanding the nonconvex optimization landscapes of their fundamental formulations. Then in the third part of my research, I will develop optimization algorithms with provable guarantees that can efficiently navigate these nonconvex landscapes and achieve the global optimality. Finally, in the final part, I will apply the alternating minimization algorithms to general tensor recovery problems and clustering problems. Part 1: Convex Optimization. In this part, we apply convex relaxations to several popular nonconvex problems in signal processing and machine learning (e.g. line spectral estimation problem and tensor decomposition problem) and prove that the solving the new convex relaxation problems can return the globally optimal solutions of their original nonconvex formulations. Part 2: Nonconvex Optimization. In this part, we focus on the fundamentally nonconvex optimization landscapes for several low-rank matrix optimization problems with general objective functions, which covers a massive number of popular problems in signal processing and machine learning. In particular, we develop mild conditions for these general low-rank matrix optimization problems to have a benign landscape: all second-order stationary points are global optimal solutions and all saddle points are strict saddles (i.e. Hessian matrix has a negative eigenvalue). Part 3: Algorithms. In this part, we will develop optimization algorithms with provable second-order optimal convergence for general nonconvex and non-Lipschitz problems. Further, in this part, we also solve an open problem for the second-order convergence of alternating minimization algorithms that have been widely used in practice to solve large-scale nonconvex problems due to their simple implementation, fast convergence, and superb empirical performance. Then the second-order convergence guarantees, along with the knowledge (see Part 2) that a massive number of nonconvex optimization problems have been shown to have a benign landscape (all second-order stationary points are global minima), ensure that the proposed algorithms can find global minima for a class of nonconvex problems. Part 4: Applications. In this part, we apply the alternating minimization algorithms to several popular applications in signal processing and machine learning, e.g., the low-rank tensor recovery problem and the spherical Principal Component Analysis (PCA).