Algorithms with conic termination for nonlinear optimization
Technical Report
·
OSTI ID:5667958
This paper describes algorithms for unconstrained optimization which have the property of minimizing conic objective functions in a finite number of steps, when line searches are exact. This work extends the algorithms of Davidon and Gourgeon and Nocedal to general nonlinear objective functions, paying much attention to the practical behavior of the new methods. Three types of algorithms are described; they are extensions of the conjugate gradient method, the BFGS method and a limited memory BFGS method. The numerical results show that new methods are very effective in solving practical problems. 19 refs., 4 tabs.
- Research Organization:
- Argonne National Lab., IL (USA)
- DOE Contract Number:
- W-31109-ENG-38
- OSTI ID:
- 5667958
- Report Number(s):
- ANL/MCS-TM-104; ON: DE88005893
- Country of Publication:
- United States
- Language:
- English
Similar Records
On collinear scaling algorithms that extend quasi-Newton methods
A limited-memory algorithm for bound-constrained optimization
On line search termination criteria for and convergence of conic algorithms for minimization
Conference
·
Fri Dec 30 23:00:00 EST 1994
·
OSTI ID:35777
A limited-memory algorithm for bound-constrained optimization
Technical Report
·
Thu Feb 29 23:00:00 EST 1996
·
OSTI ID:204262
On line search termination criteria for and convergence of conic algorithms for minimization
Conference
·
Tue Dec 31 23:00:00 EST 1985
·
OSTI ID:5606031