Improving solver success in reaching feasibility for sets of nonlinear constraints

Computers and Operations Research

Volumn 35, Issue 5, 2008, Pages 1394-1411

  Ibrahim, Walid ^a   Chinneck, John W ^b  

^a UNITED ARAB EMIRATES UNIVERSITY   (United Arab Emirates)

^b CARLETON UNIVERSITY   (Canada)

Author keywords

Constraint consensus; Heuristics; Initial point placement; Nonlinear feasibility; Point improvement

Indexed keywords

COMPUTATIONAL METHODS; HEURISTIC ALGORITHMS; MATHEMATICAL MODELS; NONLINEAR ANALYSIS; PROBLEM SOLVING; SET THEORY;

CONSTRAINT CONSENSUS; INITIAL POINT PLACEMENT; NONLINEAR FEASIBILITY;

CONSTRAINT THEORY;

EID: 34748845734     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2006.08.002     Document Type: Article

References (18)

1. Drud A.S. CONOPT-a large scale GRG code. ORSA Journal on Computing 6 (1994) 207-216
2. Lawrence C.T., and Tits A.L. A computationally efficient feasible sequential quadratic programming algorithm. SIAM Journal on Optimization 11 4 (2001) 1092-1118
3. Lasdon L.S. Optimization theory for large systems (1970), Macmillan Company, New York
4. Rardin R.L. Optimization in operations research (1998), Prentice Hall, Upper Saddle River, NJ, USA
5. Wright S.J. Primal-dual interior point methods (1997), SIAM Press, Philadelphia, PA
6. Chinneck J.W. The constraint consensus method for finding approximately feasible points in nonlinear programs. INFORMS Journal on Computing 16 3 (2003) 255-265
7. Censor Y., Gordon D., and Gordon R. Component averaging: an efficient iterative parallel algorithm for large and sparse unstructured problems. Parallel Computing 27 (2001) 777-808
8. Elwakeil O.A., and Arora J.S. Methods for finding feasible points in constrained optimization. AIAA Journal 33 (1995) 1715-1719
9. Chen X.B., and Kostreva M.M. Global convergence analysis of algorithms for finding feasible points in norm-relaxed MFD. Journal of Optimization Theory and Applications 100 2 (1999) 287-309
10. Gertz M, Nocedal J, Sartenaer A. A starting-point strategy for nonlinear interior methods. Report OTC 2003/4, Optimization Technology Center, Northwestern University, Evanston, IL, USA, March 2003.
11. Murtagh BA, Saunders MA. MINOS 5.4 User's guide. Report SOL 83-20R. Systems Optimization Laboratory, Stanford University (revised February 1995); 1983. See 〈http://www.sbsi-sol-optimize.com/〉.
12. Censor Y., and Zenios S.A. Parallel optimization: theory, algorithms, and applications (1997), Oxford University Press, New York
13. Bongartz I., Conn A.R., Gould N., and Toint P.L. CUTE: constrained and unconstrained testing environment. ACM Transactions on Mathematical Software 21 1 (1995) 123-160. http://www.sor.princeton.edu/~rvdb/ampl/nlmodels/cute/index.html See 〈http://www.sor.princeton.edu/∼rvdb/ampl/nlmodels/cute/index.html〉 for CUTE models in AMPL format
14. Fourer R., Gay D.M., and Kernighan B. AMPL: a modeling language for mathematical programming. 2nd ed. (2003), Thomson Brooks/Cole, Pacific Grove, CA, USA
15. Gill PE, Murray W, Saunders MA. SNOPT: an SQP algorithm for large-scale constrained optimization. Report SOL 97-3. Systems Optimization Laboratory, Stanford University, 1997. See 〈http://www.sbsi-sol-optimize.com/〉.
16. Waltz RA, Nocedal J. KNITRO User's manual. Technical report OTC 2003/05. Optimization Technology Center, Northwestern University, Evanston, IL, USA, April 2003. See 〈http://www.ziena.com/knitro.html〉.
17. Spellucci P. An SQP method for general nonlinear programs using only equality constrained subproblems. Mathematical Programming 82 3 (1998) 413-448
18. Gould NIM., Leyffer S., and Toint PhL. A multidimensional filter algorithm for nonlinear equations and nonlinear least squares. SIAM Journal of Optimization 15 (2004) 17-38