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"Skripsi ini mempelajari beberapa variasi dari Ketaksamaan Mayorisasi diantaranya Ketaksamaan Mayorisasi Klasik Hardy-Littlewood-Pólya dan bentuk Integral Riemann dari ketaksamaan tersebut. Ketaksamaan klasik tersebut digeneralisasi dengan menggunakan konsep relatif konveks dan sebagai hasilnya diperoleh Generalisasi Ketaksamaan Mayorisasi Hardy- Littlewood-Pólya dan bentuk Integral Riemann dari ketaksamaan tersebut. Bukti dari versi generalisasi yang diberikan disini ditemukan oleh C.P Niculescu and F. Popovici. Beberapa contoh pengaplikasian dari ketaksamaan-ketaksamaan yang dihasilkan juga akan diberikan di skripsi ini seperti bukti dari ketaksamaan Jensen dan ketaksamaaan rataan pangkat.

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This skripsi studies some variance of Majorization Inequalities such as the Classical Hardy-Littlewood-Pólya Majorization Inequality together with its Riemann integral form. The classical inequality is generalized by using the concept of relative convexity and as a result we have The Generalization of Hardy-Littlewood-Pólya Majorization Inequality together with its Riemann integral form. The proof of the generalized version given here is due to C.P Niculescu and F. Popovici. Some examples of the application of the resulting inequalities will also be given in this skripsi such as the proof of Jensen inequality and power mean inequality."

"This skripsi studies some variance of Majorization Inequalities such as theClassical Hardy-Littlewood-Pólya Majorization Inequality together with itsRiemann integral form. The classical inequality is generalized by using theconcept of relative convexity and as a result we have The Generalization ofHardy-Littlewood-Pólya Majorization Inequality together with its Riemannintegral form. The proof of the generalized version given here is due to C.PNiculescu and F. Popovici. Some examples of the application of the resultinginequalities will also be given in this skripsi such as the proof of Jenseninequality and power mean inequality."

"Discrete Convex Analysis is a novel paradigm for discrete optimization that combines the ideas in continuous optimization (convex analysis) and combinatorial optimization (matroid/submodular function theory) to establish a unified theoretical framework for nonlinear discrete optimization. The study of this theory is expanding with the development of efficient algorithms and applications to a number of diverse disciplines like matrix theory, operations research, and economics. This self-contained book is designed to provide a novel insight into optimization on discrete structures and should reveal unexpected links among different disciplines. It is the first and only English-language monograph on the theory and applications of discrete convex analysis."

"Pertidaksamaan Hadamard adalah pertidaksamaan yang dibentuk oleh integral Riemann suatu fungsi konveks pada interval tertutup dengan integrasi numerik aturan titik tengah dan aturan trapesium. Hasil pengembangan dari pertidaksamaan Hadamard untuk fungsi terturunkan dan perkalian dua fungsi disebut pertidaksamaan tipe Hadamard. Studi literatur ini bertujuan untuk mempelajari beberapa pertidaksamaan tipe Hadamard berkaitan dengan fungsi-konveksi.

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Hadamard's inequality is formed by Riemann integral form of convex function and its approximation rules by using midpoint rule and trapezoidal rule. The extension of Hadamard?s inequality for differentiable function and products of two functions is called Hadamard type. This study of literature is studying about the Hadamard type inequalities based on s-convexity."

"A study of how complexity questions in computing interact with classical mathematics in the numerical analysis of issues in algorithm design. Algorithmic designers concerned with linear and nonlinear combinatorial optimization will find this volume especially useful. Two algorithms are studied in detail: the ellipsoid method and the simultaneous diophantine approximation method. Although both were developed to study, on a theoretical level, the feasibility of computing some specialized problems in polynomial time, they appear to have practical applications. The book first describes use of the simultaneous diophantine method to develop sophisticated rounding procedures. Then a model is described to compute upper and lower bounds on various measures of convex bodies. Use of the two algorithms is brought together by the author in a study of polyhedra with rational vertices. The book closes with some applications of the results to combinatorial optimization."

"Here is a book devoted to well-structured and thus efficiently solvable convex optimization problems, with emphasis on conic quadratic and semidefinite programming. The authors present the basic theory underlying these problems as well as their numerous applications in engineering, including synthesis of filters, Lyapunov stability analysis, and structural design. The authors also discuss the complexity issues and provide an overview of the basic theory of state-of-the-art polynomial time interior point methods for linear, conic quadratic, and semidefinite programming. The book's focus on well-structured convex problems in conic form allows for unified theoretical and algorithmical treatment of a wide spectrum of important optimization problems arising in applications.Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications presents and analyzes numerous engineering models, illustrating the wide spectrum of potential applications of the new theoretical and algorithmical techniques emerging from the significant progress taking place in convex optimization. It is hoped that the information provided here will serve to promote the use of these techniques in engineering practice. The book develops a kind of "algorithmic calculus" of convex problems, which can be posed as conic quadratic and semidefinite programs. This calculus can be viewed as a "computationally tractable" version of the standard convex analysis."

"This compact book, through the simplifying perspective it presents, will take a reader who knows little of interior-point methods to within sight of the research frontier, developing key ideas that were over a decade in the making by numerous interior-point method researchers. It aims at developing a thorough understanding of the most general theory for interior-point methods, a class of algorithms for convex optimization problems. The study of these algorithms has dominated the continuous optimization literature for nearly 15 years. In that time, the theory has matured tremendously, but much of the literature is difficult to understand, even for specialists. By focusing only on essential elements of the theory and emphasizing the underlying geometry, A Mathematical View of Interior-Point Methods in Convex Optimization makes the theory accessible to a wide audience, allowing them to quickly develop a fundamental understanding of the material.The author begins with a general presentation of material pertinent to continuous optimization theory, phrased so as to be readily applicable in developing interior-point method theory. This presentation is written in such a way that even motivated Ph.D. students who have never had a course on continuous optimization can gain sufficient intuition to fully understand the deeper theory that follows. Renegar continues by developing the basic interior-point method theory, with emphasis on motivation and intuition. In the final chapter, he focuses on the relations between interior-point methods and duality theory, including a self-contained introduction to classical duality theory for conic programming; an exploration of symmetric cones; and the development of the general theory of primal-dual algorithms for solving conic programming optimization problems.Rather than attempting to be encyclopedic, A Mathematical View of Interior-Point Methods in Convex Optimization gives the reader a solid understanding of the core concepts and relations, the kind of understanding that stays with a reader long after the book is finished."

"Provides a relatively brief introduction to conjugate duality in both finite- and infinite-dimensional problems. An emphasis is placed on the fundamental importance of the concepts of Lagrangian function, saddle-point, and saddle-value. General examples are drawn from nonlinear programming, approximation, stochastic programming, the calculus of variations, and optimal control."

"This book provides a self-contained presentation of basic results of the theory of convex sets and functions in infinite-dimensional spaces. The main emphasis is on applications to convex optimization and convex optimal control problems in Banach spaces. A distinctive feature is a strong emphasis on the connection between theory and application. This edition has been updated to include new results pertaining to advanced concepts of subdifferential for convex functions and new duality results in convex programming. The last chapter, concerned with convex control problems, has been rewritten and completed with new research concerning boundary control systems, the dynamic programming equations in optimal control theory and periodic optimal control problems. "