"Control of Complex Systems: Structural Constraints and Uncertainty" focuses on control design under information structure constraints, with a particular emphasis on large-scale systems. The complexity of such systems poses serious computational challenges and severely restricts the types of feedback laws that can be used in practice. This book systematically addresses the main issues, and provides a number of applications that illustrate potential design methods, most which use Linear Matrix Inequalities (LMIs), which have become a popular design tool over the past two decades. Authors Aleksandar I. Zecevic and Dragoslav D. Siljak use their years of experience in the control field to also:

• Address the issues of large-scale systems as they relate to robust control and linear matrix inequalities
• Discuss a new approach to applying standard LMI techniques to large-scale systems, combining graphic-theoretic decomposition techniques with appropriate low-rank numerical approximations and dramatically reducing the computational effort
• Providing numerous examples and a wide variety of applications, ranging from electric power systems and nonlinear circuits to mechanical problems and dynamic Boolean networks

"Control of Complex Systems: Structural Constraints and Uncertainty" will appeal to practicing engineers, researchers and students working in control design and other related areas.

"Control of Complex Systems: Structural Constraints and Uncertainty" focuses on control design under information structure constraints, with a particular emphasis on large-scale systems. The complexity of such systems poses serious computational challenges and severely restricts the types of feedback laws that can be used in practice. This book systematically addresses the main issues, and provides a number of applications that illustrate potential design methods, most which use Linear Matrix Inequalities (LMIs), which have become a popular design tool over the past two decades. Authors Aleksandar I. Zecevic and Dragoslav D. Siljak use their years of experience in the control field to also:

• Address the issues of large-scale systems as they relate to robust control and linear matrix inequalities
• Discuss a new approach to applying standard LMI techniques to large-scale systems, combining graphic-theoretic decomposition techniques with appropriate low-rank numerical approximations and dramatically reducing the computational effort
• Providing numerous examples and a wide variety of applications, ranging from electric power systems and nonlinear circuits to mechanical problems and dynamic Boolean networks

"Control of Complex Systems: Structural Constraints and Uncertainty" will appeal to practicing engineers, researchers and students working in control design and other related areas.

Is it rational for scientifically trained individuals to believe in God, and accept controversial theological claims such as the existence of miracles? Are science and theology essentially incompatible, or can their positions be reconciled on some level? Truth, Beauty, and the Limits of Knowledge: A Path from Science to Religion addresses such questions by recasting certain key religious teachings in a language that is familiar to scientists, engineers, and mathematicians. It does so with the help of various science-based metaphors and analogies, whose primary purpose is to interpret theological claims in a way that is attuned to the spirit of our age. A crucial step in developing such "analogical bridges" between science and religion involves challenging the traditional Newtonian paradigm, which maintains that physical processes are generally deterministic and predictable (i.e., "well behaved"). A closer examination of recent scientific developments will show that this assumption is incorrect, and that certain aspects of nature will remain unknowable to us regardless of future technological advances. This realization opens the door to a meaningful conversation between science and theology, since both disciplines implicitly accept the premise that the true nature of "reality" can never be fully grasped by the human mind.