.png){kind=icon}
Return
Higher-order interior point methods for convex nonlinear programming
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compchemeng.2023.108475.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper extends the concept of higher-order search directions within interior point methods to convex nonlinear programming. This includes the mathematical framework needed to compute the higher-order derivatives. The paper also highlights some special cases where the computation of these higher-order derivatives is simplified and a dimensional lifting procedure for transforming a large number of general nonlinear problems into one of these more efficient forms. The paper further describes the algorithmic development required to employ these higher-order search directions in a practical algorithm. Computational results are presented for a large number of test problems, highlighting higher-order methods' strong potential for decreasing iteration count and their case-by-case potential for decreasing CPU time.
Keywords:
Interior point methods
Higher-order search directions
Convex programming
Nonlinear programming
Journal
C
IF:
3.9
Papers:
8.1K
Citations:
1.7W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSave
View more