In 1984, N. Karmarkar published a seminal paper on algorithmic linear programming. During the subsequent decade, it stimulated a huge outpouring of new algorithmic results by researchers world-wide in many areas of mathematical programming and numerical computation. This book gives an overview of the resulting, dramatic reorganization that has occurred in one of these areas: algorithmic differentiable optimization and equation-solving, or, more simply, algorithmic differentiable programming. The book is aimed at readers familiar with advanced calculus, numerical analysis, in particular numerical linear algebra, the theory and algorithms of linear and nonlinear programming, and the fundamentals of computer science, in particular, computer programming and the basic models of computation and complexity theory. J.L. Nazareth is a Professor in the Department of Pure and Applied Mathematics at Washington State University. He is the author of two books previously published by Springer-Verlag, DLP and Extensions: An Optimization Model and Decision Support System (2001) and The Newton-Cauchy Framework: A Unified Approach to Unconstrained Nonlinear Minimization (1994).
Significant research activities have taken place in the areas of local and global optimization in the last two decades. Many new theoretical, computational, algorithmic, and software contributions have resulted. It has been realized that despite these numerous contributions, there does not exist a systematic forum for thorough experimental computational testing and. evaluation of the proposed optimization algorithms and their implementations. Well-designed nonconvex optimization test problems are of major impor tance for academic and industrial researchers interested in algorithmic and software development. It is remarkable that eventhough nonconvex models dominate all the important application areas in engineering and applied sci ences, there is only a limited dass of reported representative test problems. This book reflects our long term efforts in designing a benchmark database and it is motivated primarily from the need for nonconvex optimization test problems. The present collection of benchmarks indudes test problems from literature studies and a large dass of applications that arise in several branches of engineering and applied science."
Computational Optimization: A Tribute to Olvi Mangasarian serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
New Mexico is a land of crisis; steps must be taken to improve the lives of its residents. In "In Search of a Day in Paradise: Aztlan," author Dr. Moises Venegas analyzes the history of Hispanics in the southwest and makes a call for change in New Mexico's education, policies, and politics.
Venegas shows that after four hundred years, mestizo Hispanos are still searching for their elusive day in paradise-that cultural, economic, political and educational paradise that could help put them in a better place in the future. "In Search of a Day in Paradise: Aztlan" discusses how, in this modern era, New Mexicans can strive for the return of Aztlan-New Mexico, Texas, Colorado, Arizona, and California-by demanding a better education, voting for leaders who do not just talk but act when it comes to improving the job situation in New Mexico, and eliminating poverty.
"In Search of a Day in Paradise: Aztlan" offers insight into how using historical data can be of influence as Hispanos seek to improve their standing in today's society. Time will tell if they will perform better educationally and politically in 2075 than they have in the past.
For both public and private managers, the book Optimization Methods for a Stakeholder Society is today's key to answer the problem of a sustainable development world. This world has to take into account the meaning of all stakeholders involved and has to reconcile a number of objectives, such as economic growth, employment and preservation of the ecosystem. Traditional methods, such as cost-benefit, are outmoded as they translate all these objectives into monetary costs, a materialistic approach. On the contrary, objectives have rather to stick to their own units, eventually indicators.
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