General convex relaxations of implicit functions and inverse functions-Reference-Cited by-同舟云学术

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General convex relaxations of implicit functions and inverse functions

Published:2023-04-10 Issue:3 Volume:86 Page:545-572
ISSN:0925-5001
Container-title:Journal of Global Optimization
language:en
Short-container-title:J Glob Optim

Author:

Cao Huiyi,Khan Kamil A.^ORCID

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Management Science and Operations Research,Control and Optimization,Computer Science Applications,Business, Management and Accounting (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s10898-023-01281-0.pdf

Reference48 articles.

1. Mitsos, A., Chachuat, B., Barton, P.I.: McCormick-based relaxations of algorithms. SIAM J. Optim. 20(2), 573–601 (2009)

2. Adjiman, C.S., Dallwig, S., Floudas, C.A., Neumaier, A.: A global optimization method, $$\alpha $$BB, for general twice-differentiable constrained NLPs - I. Theor. Adv. Comput. Chem. Eng. 22(9), 1137–1158 (1998)

3. McCormick, G.P.: Computability of global solutions to factorable nonconvex programs: part I-convex underestimating problems. Math. Program. 10(1), 147–175 (1976)

4. Khan, K.A., Watson, H.A.J., Barton, P.I.: Differentiable McCormick relaxations. J. Global Optim. 67(4), 687–729 (2017)

5. Chachuat, B.: MC++: a toolkit for bounding factorable functions (2014)

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