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ABSTRACTProblem Solving in Endodontics, 5th Edition, by James L. Gutmann and Paul Lovdahl, offers updated techniques and an evidence-based approach to the most common procedures performed at chairside. Ideal for both endodontists and general dentists, this thoroughly revised reference combines the precision of quality endodontic care with achievable and pain-free outcomes for the patient. Each chapter has been carefully designed so that you'll quickly grasp the anatomy, the instruments needed, and what procedures should be performed -- all supplemented by boxed clinical case examples and tips. Going beyond problem solving, it also addresses the major issues in diagnostic, anatomic, restorative, periodontic, traumatic, and surgical aspects of tooth retention. Provides chairside guidance for the endodontic procedures most commonly performed by endodontists and general dentists. Entire text has expanded concepts that are verified in new drawings and clinically relevant cases. Integrates new technologies and materials into every chapter that when applied result in predictable and optimal outcomes. Establishes clear parameters for the retention of teeth. Correlates optimal patient outcomes with an evidence-based approach. Reflects the practical expertise of renowned endodontics authority and past President of the AAE, Dr. James Gutmann, and endodontics specialist, Dr. Paul Lovdahl."

"Large-scale problems of engineering and scientific computing often require solutions of eigenvalue and related problems. This book gives a unified overview of theory, algorithms, and practical software for eigenvalue problems. It organizes this large body of material to make it accessible for the first time to the many nonexpert users who need to choose the best state-of-the-art algorithms and software for their problems. Using an informal decision tree, just enough theory is introduced to identify the relevant mathematical structure that determines the best algorithm for each problem.The algorithms and software at the "leaves" of the decision tree range from the classical QR algorithm, which is most suitable for small dense matrices, to iterative algorithms for very large generalized eigenvalue problems. Algorithms are presented in a unified style as templates, with different levels of detail suitable for readers ranging from beginning students to experts. The authors' comprehensive treatment includes a treasure of further bibliographic information."