scholarly journals PERBANDINGAN METODE SEPARABLE PROGRAMMING DAN QUADRATIC PROGRAMMING DALAM PEMECAHAN MASALAH PEMROGRAMAN NONLINIER

2019 ◽  
Vol 8 (4) ◽  
pp. 277
Author(s):  
I GEDE WIKAN ADIWIGUNA ◽  
G.K GANDHIADI ◽  
NI MADE ASIH

The Separable programming method solves nonlinear programming problems by transforming a nonlinear shape that consists of a single variable into a linear function and resolved by the simplex method. Meanwhile, the quadratic programming method accomplishes the two degrees nonlinear model by transforming the nonlinear shape into linear function with the Kuhn Tucker Conditions and resolved by the simplex Wolfe method. Both of these methods are applied to the Markowitz’s portfolio model, which is to find the proportion of stock funds to obtain maximum profits by combination of three shares, such as BMRI, GGRM, and ICBP. The completion using the quadratic programming method is more effective and efficient with the same optimum value.

Author(s):  
Paul Armand ◽  
Dominique Orban

In this short paper, we recall the use of squared slacks used to transform inequality constraints into equalities and several reasons why their introduction may be harmful in many algorithmic frameworks routinely used in nonlinear programming. Numerical examples performed with the sequential quadratic programming method illustrate those reasons. Our results are reproducible with state-of-the-art implementations of the methods concerned and mostly serve a pedagogical purpose, which we believe will be useful not only to practitioners and students, but also to researchers. 


2019 ◽  
Vol 15 (2) ◽  
pp. 38-42
Author(s):  
O.S. Goncharenko ◽  
V.N. Gladilin ◽  
L. Šiaudinytė

2019 ◽  
Vol 158 (4) ◽  
pp. 145
Author(s):  
Toshiya Ueta ◽  
Hiroyuki Mito ◽  
Masaaki Otsuka ◽  
Yoshikazu Nakada ◽  
Blair C. Conn ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document