2014
DOI: 10.1117/12.2061975
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Using saddle points for challenging optical design tasks

Abstract: The present research is part of an effort to develop tools that make the lens design process more systematic. In typical optical design tasks, the presence of many local minima in the optical merit function landscape makes design non-trivial. With the method of Saddle Point Construction (SPC) which was developed recently ([F. Bociort and M. van Turnhout, Opt. Engineering 48, 063001 (2009)]) new local minima are obtained efficiently from known ones by adding and removing lenses in a systematic way. To illustrat… Show more

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Cited by 4 publications
(5 citation statements)
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“…Combining SPC with conventional design methods, as shown in Fig. 6, can lead to good complex designs starting from simpler ones [15].…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…Combining SPC with conventional design methods, as shown in Fig. 6, can lead to good complex designs starting from simpler ones [15].…”
Section: Discussionmentioning
confidence: 99%
“…Excepting simple problems, no known global optimization method can guarantee finding all (good) solutions in a reasonable time, and SPC is no exception. However, high-quality designs have already been obtained with the special [12,13] and with the general versions [15] of SPC. SPC will be finally validated if many high-quality designs will be obtained in this way.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The lens model used in this paper is a modified version of a wide-angle pinhole lens with a full FOV of 90 • [21]. The system is smaller than 15 mm in length and the pinhole has a diameter of 1 mm.…”
Section: Modelmentioning
confidence: 99%
“…In order to see whether this ability of SPC to find many new system shapes can also be observed in problems that are very different from the special case mentioned above, we have first examined the landscape of a wide-angle pinhole lens with a full field of 110 degrees 8 . With other methods (see below) we find three stable solutions (i.e.…”
Section: Decomposing the High-dimensional Search For New Local Minimamentioning
confidence: 99%