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"Hingga saat ini, pengembangan Metode Simpleks terus dilakukan untuk mendapatkan algoritma efisien yang dapat mengurangi iterasi dan waktu komputasi. Penelitian ini dilakukan sebagai hasil pengembangan dari penelitian terdahulu dan difokuskan pada pembentukkan algoritma untuk mendapatkan Basic Feasible Solution (BFS) karena penggunaan BFS pada Metode Simpleks terbukti dapat mengurangi iterasi. Algoritma yang dikembangkan menggunakan Kriteria Cosinus serta Eliminasi Gaussian dalam mendapatkan BFS dan diuji pada 13 kasus yang berasal dari jurnal-jurnal Programa Linier. Di akhir skripsi, dibahas hasil dari penelitian ini yang menunjukkan bahwa algoritma yang dikembangkan dapat digunakan untuk menyelesaikan masalah Programa Linier, dan dapat mengurangi jumlah iterasi yang dilakukan.

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Up until now, researches in developing Simplex Method are done continually to get the most efficient algorithm to reduce iteration and computation time in solving Linear Programming. This research is held as a development of previous researches and focused in forming algorithm to get Basic Feasible Solution (BFS) because using BFS in Simplex Method has been proved in reducing iteration. This algorithm uses Cosine Criterion and Gaussian Elimination to get BFS and is tested by solving 13 problems which are gained from Journal about Linear Programming. At the end, this paper discusses the result from this research which shows that this algorithm can be used to solve Linear Programming and reduce iteration. In solving Linear Programming."

"Prosedur untuk mengalokasikan ruangan kelas dalam sebuah institusi pendidikan diketengahkan. Prosedur ini didasarkan kepada model program linier yang meminimumkan fungsi tujuan. Dengan harga-harga baku (tetapan) dan beberapa parameter yang dihasilkan oleh prosedur, model pertama kali menempatkan sejumlah ruang yang ada dan memungkinkan sesuai dengan permintaan. Kemudian prosedur menempatkan permintaan ke ruangan yang paling sesuai dengan tuntutan permintaan. Dengan mengubah-ubah harga tetapan dan fungsi tujuan, pemakai dapat pula mengajukan beberapa permintaan untuk menempati ruangan yang lebih disukai. Kendala-kendala dikaitkan dengan kemungkinan pemakaian ruangan pada jam-jam yang bervariasi dalam satu hari, dan dengan permintaan-permintaan terhadap ruangan-ruangan tersebut. Selama pendekatan ini secara implisit menghasilkan semua pengalokasian, ia harus menghasilkan hasil yang lebih baik daripada alokasi secara manual."

"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."

"A reprint of the original volume, which won the Lanchester Prize awarded by the Operations Research Society of America for the best work of 1968. Although out of print for nearly 15 years, it remains one of the most referenced volumes in the field of mathematical programming. Recent interest in interior point methods generated by Karmarkar's Projective Scaling Algorithm has created a new demand for this book because the methods that have followed from Karmarkar's bear a close resemblance to those described. There is no other source for the theoretical background of the logarithmic barrier function and other classical penalty functions. Analyzes in detail the "central" or "dual" trajectory used by modern path following and primal/dual methods for convex and general linear programming. As researchers begin to extend these methods to convex and general nonlinear programming problems, this book will become indispensable to them."