2010
DOI: 10.1117/12.853924
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Systematics of the design shapes in the optical merit function landscape

Abstract: In this paper we describe new properties of the design landscape that could lead in the future to a new way to determine good starting points for subsequent local optimization. While in optimization the focus is usually only on local minima, here we show that points selected in the vicinity of other types of critical points (i.e. points where the merit function gradient vanishes) can be very useful starting points. We study here a problem that is simple enough to be analyzed in detail, the design landscape of … Show more

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Cited by 3 publications
(11 citation statements)
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“…In a study of a thin-film design landscape (that will be discussed in detail elsewhere) it was found that minima form complex, almost random patterns, but critical points with indices higher than 1 form regular patterns that are useful for understanding the topology of the landscape and for finding the minima systematically. For the triplet problem under investigation here, a set of critical points having indices 0, 1 and 2 called fundamental critical points have a remarkable property that shows the existence of order in the design landscape 6 .…”
Section: Critical Pointsmentioning
confidence: 99%
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“…In a study of a thin-film design landscape (that will be discussed in detail elsewhere) it was found that minima form complex, almost random patterns, but critical points with indices higher than 1 form regular patterns that are useful for understanding the topology of the landscape and for finding the minima systematically. For the triplet problem under investigation here, a set of critical points having indices 0, 1 and 2 called fundamental critical points have a remarkable property that shows the existence of order in the design landscape 6 .…”
Section: Critical Pointsmentioning
confidence: 99%
“…6 we have presented two analytical models that can be used to calculate simple approximations for the fundamental critical points of the triplet landscape. When the stop is at the lens, spherical aberration is the most nonlinear of the Seidel aberrations, and determines the non-convex character of the landscape.…”
Section: Critical -Point Projectionmentioning
confidence: 99%
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