TAILIEUCHUNG - Handbook of algorithms for physical design automation part 12

Handbook of Algorithms for Physical Design Automation part 12 provides a detailed overview of VLSI physical design automation, emphasizing state-of-the-art techniques, trends and improvements that have emerged during the previous decade. After a brief introduction to the modern physical design problem, basic algorithmic techniques, and partitioning, the book discusses significant advances in floorplanning representations and describes recent formulations of the floorplanning problem. The text also addresses issues of placement, net layout and optimization, routing multiple signal nets, manufacturability, physical synthesis, special nets, and designing for specialized technologies. It includes a personal perspective from Ralph Otten as he looks back on. | 92 Handbook of Algorithms for Physical Design Automation There are many examples of convex functions. Any linear function is convex. Other examples include the commonly seen univariate functions x ex x2 as well as multivariate functions a x b AxH2 where A a and b are given data matrix vector constant. We say f is concave if f is convex. The entropy function 2i x log xt is a concave function over . Iff is continuously differentiable then the convexity of f is equivalent to f y f x Vf x T y x V x y eW In other words the first-order Taylor series expansion serves as a global under-estimator of f. In addition iff is twice continuously differentiable then the convexity off is equivalent to the positive semidefiniteness of its Hessian matrix V2f x 0 V x e r The above criterion shows that a linear function is always convex while a quadratic function xTPx aTx b is convex if and only if P 0. Notice that the linear plus the constant term aTx b do not have any bearing on the convexity or the lack of of f. A function f is said to be concave if f is convex. One can think of numerous examples of functions which are neither convex nor concave. For instance the function x3 is convex over 0 rc and concave over the region rc 0 but is neither convex nor concave over . The most important property about convex functions is the fact that they are closed under summation positive scaling and the point-wise maximum operations. In particular if the f s are convex then so is maxi f x even though it is typically nondifferentiable . A notable connection between convex set and convex function is the fact that the level sets of any convex function f x are always convex that is x f x c is convex for any c e . The converse is not true however. For example the function f x y F is nonconvex but its level sets are convex. Convex Optimization Problems Consider a generic optimization problem in the minimization form minimize f0 x subject to fi x 0 i 1 2 . m hj x 0 j 1 2 . r x e S where f0 is

TỪ KHÓA LIÊN QUAN
TAILIEUCHUNG - Chia sẻ tài liệu không giới hạn
Địa chỉ : 444 Hoang Hoa Tham, Hanoi, Viet Nam
Website : tailieuchung.com
Email : tailieuchung20@gmail.com
Tailieuchung.com là thư viện tài liệu trực tuyến, nơi chia sẽ trao đổi hàng triệu tài liệu như luận văn đồ án, sách, giáo trình, đề thi.
Chúng tôi không chịu trách nhiệm liên quan đến các vấn đề bản quyền nội dung tài liệu được thành viên tự nguyện đăng tải lên, nếu phát hiện thấy tài liệu xấu hoặc tài liệu có bản quyền xin hãy email cho chúng tôi.
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.