In honor of tax season, the price of this book will be cut to only $7.00. But hurry and get yours today because come April 15th, the price goes back to $9.99.****************************************No matter what industry a business is in, the one thing that most business owners avoid is bookkeeping. This book was designed specifically for those people who choose to avoid doing their books on a regular basis. It was created for small business owners and office managers to help quickly and easily catch up a company's books just in time for tax season. This book includes: * 11 Things You Will Need to Collect Before You Start* 10 Simple Steps to Enter Your Data * Tricks to Make QuickBooks do the Data Entry For You* A Simple Technique to Double Check and Reconcile your Work* And a List of What Your Accountant will Expect From You. This book is a quick easy read with lots of illustrated examples and very little "Industry Jargon." If you are looking for a fast read that will help you catch your books up at lightening speed, then this is the book for you. Oh...and Bookkeepers will like it too.
Since I started working in the area of nonlinear programming and, later on, variational inequality problems, I have frequently been surprised to find that many algorithms, however scattered in numerous journals, monographs and books, and described rather differently, are closely related to each other. This book is meant to help the reader understand and relate algorithms to each other in some intuitive fashion, and represents, in this respect, a consolidation of the field. The framework of algorithms presented in this book is called Cost Approxi- mation. (The preface of the Ph.D. thesis [Pat93d] explains the background to the work that lead to the thesis, and ultimately to this book.) It describes, for a given formulation of a variational inequality or nonlinear programming problem, an algorithm by means of approximating mappings and problems, a principle for the update of the iteration points, and a merit function which guides and monitors the convergence of the algorithm. One purpose of this book is to offer this framework as an intuitively appeal- ing tool for describing an algorithm. One of the advantages of the framework, or any reasonable framework for that matter, is that two algorithms may be easily related and compared through its use. This framework is particular in that it covers a vast number of methods, while still being fairly detailed; the level of abstraction is in fact the same as that of the original problem statement.
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