Abstract

In lens design, damped least-squares methods are typically used to find the nearest local minimum to a starting configuration in the merit function landscape. In this paper, we explore the use of such a method for a purpose that goes beyond local optimization. The merit function barrier, which separates an unsatisfactory solution from a neighboring one that is better, can be overcome by using low damping and by allowing the merit function to temporarily increase. However, such an algorithm displays chaos, chaotic transients and other types of complex behavior. A successful escape of the iteration trajectory from a poor local minimum to a better one is associated with a crisis phenomenon that transforms a chaotic attractor into a chaotic saddle. The present analysis also enables a better understanding of peculiarities encountered with damped least-squares algorithms in conventional local optimization tasks.

© 2009 Optical Society of America

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References

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  1. G. W. Forbes and A. E. Jones, “Towards global optimization with adaptive simulated annealing,” in 1990 Intl Lens Design Conf, G. N. Lawrence, ed.,  vol. 1354 of Proc. SPIE, 144–153 (1991).
    [CrossRef]
  2. T. G. Kuper and T. I. Harris, “Global optimization for lens design - an emerging technology,” in Lens and optical system design, H. Zuegge, ed.,  vol. 1780 of Proc. SPIE, 14–28 (1992).
  3. M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens design: Global optimization with Escape Function,” Optical Review (Japan) 6, 463–470 (1995).
  4. K. E. Moore, “Algorithm for global optimization of optical systems based on genetic competition,” in Optical Design and Analysis Software, R. C. Juergens, ed.,  vol. 3780 of Proc. SPIE, 40–47 (1999).
    [CrossRef]
  5. L. W. Jones, S. H. Al-Sakran, and J. R. Koza, “Automated synthesis of both the topology and numerical parameters for seven patented optical lens systems using genetic programming,” in Current Developments in Lens Design and Optical Engineering VI, P. Z. Mouroulis, W. J. Smith, and R. B. Johnson, eds.,  vol. 5874 of Proc. SPIE, 587403 (2005).
    [CrossRef]
  6. J. P. McGuire, “Designing easily manufactured lenses using a global method,” in International Optical Design Conference 2006, G. G. Gregory, J. M. Howard, and R. J. Koshel, eds.,  vol. 6342 of Proc. SPIE, 63420O (2006).
    [CrossRef]
  7. J. R. Rogers, “Using global synthesis to find tolerance-insensitive design forms,” in International Optical Design Conference 2006, G. G. Gregory, J. M. Howard, and R. J. Koshel, eds.,  vol. 6342 of Proc. SPIE, 63420M (2006).
    [CrossRef]
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    [CrossRef]
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  13. D. Shafer, “How to optimize complex lens designs,” Laser Focus World 29, 135–138 (1993).
  14. D. Shafer, “Global optimization in optical design,” Computers in Physics 8, 188–195 (1994).
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    [CrossRef] [PubMed]
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    [CrossRef]
  19. S. N. Rasband, Chaotic dynamics of nonlinear systems (Wiley, New York, 1990).
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    [CrossRef]
  21. B. Davies, “Period Doubling,” in Encyclopedia of Nonlinear Science, A. Scott, ed. (Routledge, New York, 2004).
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    [CrossRef] [PubMed]
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    [CrossRef]

2009 (1)

2007 (1)

2006 (2)

J. P. McGuire, “Designing easily manufactured lenses using a global method,” in International Optical Design Conference 2006, G. G. Gregory, J. M. Howard, and R. J. Koshel, eds.,  vol. 6342 of Proc. SPIE, 63420O (2006).
[CrossRef]

J. R. Rogers, “Using global synthesis to find tolerance-insensitive design forms,” in International Optical Design Conference 2006, G. G. Gregory, J. M. Howard, and R. J. Koshel, eds.,  vol. 6342 of Proc. SPIE, 63420M (2006).
[CrossRef]

2005 (2)

A. Serebriakov, F. Bociort, and J. Braat, “Finding new local minima by switching merit functions in optical system optimization,” Opt. Eng. 44, 100,501 (2005).
[CrossRef]

L. W. Jones, S. H. Al-Sakran, and J. R. Koza, “Automated synthesis of both the topology and numerical parameters for seven patented optical lens systems using genetic programming,” in Current Developments in Lens Design and Optical Engineering VI, P. Z. Mouroulis, W. J. Smith, and R. B. Johnson, eds.,  vol. 5874 of Proc. SPIE, 587403 (2005).
[CrossRef]

2000 (1)

I. Castillo and E. R. Barnes, “Chaotic behavior of the affine scaling algorithm for linear programming,” SIAM J. on Optimization 11, 781–795 (2000).
[CrossRef]

1999 (1)

K. E. Moore, “Algorithm for global optimization of optical systems based on genetic competition,” in Optical Design and Analysis Software, R. C. Juergens, ed.,  vol. 3780 of Proc. SPIE, 40–47 (1999).
[CrossRef]

1996 (1)

H. E. Nusse and J. A. Yorke, “Basins of attraction,” Science 271, 1376–1380 (1996).
[CrossRef]

1995 (1)

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens design: Global optimization with Escape Function,” Optical Review (Japan) 6, 463–470 (1995).

1994 (2)

Y.-C. Lai and C. Grebogi, “Converting transient chaos into sustained chaos by feedback control,” Phys. Rev. E 49, 1094–1098 (1994).
[CrossRef]

D. Shafer, “Global optimization in optical design,” Computers in Physics 8, 188–195 (1994).

1993 (1)

D. Shafer, “How to optimize complex lens designs,” Laser Focus World 29, 135–138 (1993).

1992 (1)

T. G. Kuper and T. I. Harris, “Global optimization for lens design - an emerging technology,” in Lens and optical system design, H. Zuegge, ed.,  vol. 1780 of Proc. SPIE, 14–28 (1992).

1991 (1)

G. W. Forbes and A. E. Jones, “Towards global optimization with adaptive simulated annealing,” in 1990 Intl Lens Design Conf, G. N. Lawrence, ed.,  vol. 1354 of Proc. SPIE, 144–153 (1991).
[CrossRef]

1987 (1)

C. Grebogi, E. Ott, and J. A. Yorke, “Chaos, strange attractors, and fractal basin boundaries in non-linear dynamics,” Science 238, 632–638 (1987).
[CrossRef] [PubMed]

Al-Sakran, S. H.

L. W. Jones, S. H. Al-Sakran, and J. R. Koza, “Automated synthesis of both the topology and numerical parameters for seven patented optical lens systems using genetic programming,” in Current Developments in Lens Design and Optical Engineering VI, P. Z. Mouroulis, W. J. Smith, and R. B. Johnson, eds.,  vol. 5874 of Proc. SPIE, 587403 (2005).
[CrossRef]

Barnes, E. R.

I. Castillo and E. R. Barnes, “Chaotic behavior of the affine scaling algorithm for linear programming,” SIAM J. on Optimization 11, 781–795 (2000).
[CrossRef]

Blechinger, F.

H. Gross, H. Zügge, M. Peschka, and F. Blechinger, Principles of optimization, in Handbook of Optical Systems, vol. 3, 291–370 (Wiley-VCH, Weinheim, 2007).

Bociort, F.

Braat, J.

A. Serebriakov, F. Bociort, and J. Braat, “Finding new local minima by switching merit functions in optical system optimization,” Opt. Eng. 44, 100,501 (2005).
[CrossRef]

Castillo, I.

I. Castillo and E. R. Barnes, “Chaotic behavior of the affine scaling algorithm for linear programming,” SIAM J. on Optimization 11, 781–795 (2000).
[CrossRef]

Davies, B.

B. Davies, “Period Doubling,” in Encyclopedia of Nonlinear Science, A. Scott, ed. (Routledge, New York, 2004).

Forbes, G. W.

G. W. Forbes and A. E. Jones, “Towards global optimization with adaptive simulated annealing,” in 1990 Intl Lens Design Conf, G. N. Lawrence, ed.,  vol. 1354 of Proc. SPIE, 144–153 (1991).
[CrossRef]

Grebogi, C.

Y.-C. Lai and C. Grebogi, “Converting transient chaos into sustained chaos by feedback control,” Phys. Rev. E 49, 1094–1098 (1994).
[CrossRef]

C. Grebogi, E. Ott, and J. A. Yorke, “Chaos, strange attractors, and fractal basin boundaries in non-linear dynamics,” Science 238, 632–638 (1987).
[CrossRef] [PubMed]

Gross, H.

H. Gross, H. Zügge, M. Peschka, and F. Blechinger, Principles of optimization, in Handbook of Optical Systems, vol. 3, 291–370 (Wiley-VCH, Weinheim, 2007).

Hanson, R. J.

C. L. Lawson and R. J. Hanson, Solving Least Squares Problems (Prentice-Hall, Englewood Cliffs, NJ, 1974).

Harris, T. I.

T. G. Kuper and T. I. Harris, “Global optimization for lens design - an emerging technology,” in Lens and optical system design, H. Zuegge, ed.,  vol. 1780 of Proc. SPIE, 14–28 (1992).

Hiraga, K.

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens design: Global optimization with Escape Function,” Optical Review (Japan) 6, 463–470 (1995).

Ishikawa, J.

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens design: Global optimization with Escape Function,” Optical Review (Japan) 6, 463–470 (1995).

Isshiki, M.

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens design: Global optimization with Escape Function,” Optical Review (Japan) 6, 463–470 (1995).

Jones, A. E.

G. W. Forbes and A. E. Jones, “Towards global optimization with adaptive simulated annealing,” in 1990 Intl Lens Design Conf, G. N. Lawrence, ed.,  vol. 1354 of Proc. SPIE, 144–153 (1991).
[CrossRef]

Jones, L. W.

L. W. Jones, S. H. Al-Sakran, and J. R. Koza, “Automated synthesis of both the topology and numerical parameters for seven patented optical lens systems using genetic programming,” in Current Developments in Lens Design and Optical Engineering VI, P. Z. Mouroulis, W. J. Smith, and R. B. Johnson, eds.,  vol. 5874 of Proc. SPIE, 587403 (2005).
[CrossRef]

Koza, J. R.

L. W. Jones, S. H. Al-Sakran, and J. R. Koza, “Automated synthesis of both the topology and numerical parameters for seven patented optical lens systems using genetic programming,” in Current Developments in Lens Design and Optical Engineering VI, P. Z. Mouroulis, W. J. Smith, and R. B. Johnson, eds.,  vol. 5874 of Proc. SPIE, 587403 (2005).
[CrossRef]

Kuper, T. G.

T. G. Kuper and T. I. Harris, “Global optimization for lens design - an emerging technology,” in Lens and optical system design, H. Zuegge, ed.,  vol. 1780 of Proc. SPIE, 14–28 (1992).

Lai, Y.-C.

Y.-C. Lai and C. Grebogi, “Converting transient chaos into sustained chaos by feedback control,” Phys. Rev. E 49, 1094–1098 (1994).
[CrossRef]

Laikin, M.

M. Laikin, The method of lens design, Lens design, 4th ed. (CRC Press, Boca Raton, FL, 1996).

Lawson, C. L.

C. L. Lawson and R. J. Hanson, Solving Least Squares Problems (Prentice-Hall, Englewood Cliffs, NJ, 1974).

Marinescu, O.

McGuire, J. P.

J. P. McGuire, “Designing easily manufactured lenses using a global method,” in International Optical Design Conference 2006, G. G. Gregory, J. M. Howard, and R. J. Koshel, eds.,  vol. 6342 of Proc. SPIE, 63420O (2006).
[CrossRef]

Moore, K. E.

K. E. Moore, “Algorithm for global optimization of optical systems based on genetic competition,” in Optical Design and Analysis Software, R. C. Juergens, ed.,  vol. 3780 of Proc. SPIE, 40–47 (1999).
[CrossRef]

Nakadate, S.

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens design: Global optimization with Escape Function,” Optical Review (Japan) 6, 463–470 (1995).

Nusse, H. E.

H. E. Nusse and J. A. Yorke, “Basins of attraction,” Science 271, 1376–1380 (1996).
[CrossRef]

Ono, H.

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens design: Global optimization with Escape Function,” Optical Review (Japan) 6, 463–470 (1995).

Ott, E.

C. Grebogi, E. Ott, and J. A. Yorke, “Chaos, strange attractors, and fractal basin boundaries in non-linear dynamics,” Science 238, 632–638 (1987).
[CrossRef] [PubMed]

E. Ott, Chaos in dynamical systems, 2nd ed. (Cambridge University Press, Cambridge, 2002).

Peschka, M.

H. Gross, H. Zügge, M. Peschka, and F. Blechinger, Principles of optimization, in Handbook of Optical Systems, vol. 3, 291–370 (Wiley-VCH, Weinheim, 2007).

Rasband, S. N.

S. N. Rasband, Chaotic dynamics of nonlinear systems (Wiley, New York, 1990).

Rogers, J. R.

J. R. Rogers, “Using global synthesis to find tolerance-insensitive design forms,” in International Optical Design Conference 2006, G. G. Gregory, J. M. Howard, and R. J. Koshel, eds.,  vol. 6342 of Proc. SPIE, 63420M (2006).
[CrossRef]

Serebriakov, A.

A. Serebriakov, F. Bociort, and J. Braat, “Finding new local minima by switching merit functions in optical system optimization,” Opt. Eng. 44, 100,501 (2005).
[CrossRef]

Shafer, D.

D. Shafer, “Global optimization in optical design,” Computers in Physics 8, 188–195 (1994).

D. Shafer, “How to optimize complex lens designs,” Laser Focus World 29, 135–138 (1993).

Sinclair, D. C.

D. C. Sinclair, “Optical design software,” in Handbook of Optics, Fundamentals, Techniques, and Design, M. Bass, E. W. Van Stryland, D. R. Williams, and Wolfe W. L., eds., vol. 1, 2nd ed., 34.1–34.26 (McGraw-Hill, New York, 1995).

Turnhout, M. van

Yorke, J. A.

H. E. Nusse and J. A. Yorke, “Basins of attraction,” Science 271, 1376–1380 (1996).
[CrossRef]

C. Grebogi, E. Ott, and J. A. Yorke, “Chaos, strange attractors, and fractal basin boundaries in non-linear dynamics,” Science 238, 632–638 (1987).
[CrossRef] [PubMed]

Zügge, H.

H. Gross, H. Zügge, M. Peschka, and F. Blechinger, Principles of optimization, in Handbook of Optical Systems, vol. 3, 291–370 (Wiley-VCH, Weinheim, 2007).

Appl. Opt. (1)

Computers in Physics (1)

D. Shafer, “Global optimization in optical design,” Computers in Physics 8, 188–195 (1994).

Laser Focus World (1)

D. Shafer, “How to optimize complex lens designs,” Laser Focus World 29, 135–138 (1993).

Opt. Eng. (1)

A. Serebriakov, F. Bociort, and J. Braat, “Finding new local minima by switching merit functions in optical system optimization,” Opt. Eng. 44, 100,501 (2005).
[CrossRef]

Opt. Express (1)

Optical Review (Japan) (1)

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens design: Global optimization with Escape Function,” Optical Review (Japan) 6, 463–470 (1995).

Phys. Rev. E (1)

Y.-C. Lai and C. Grebogi, “Converting transient chaos into sustained chaos by feedback control,” Phys. Rev. E 49, 1094–1098 (1994).
[CrossRef]

Proc. SPIE (6)

K. E. Moore, “Algorithm for global optimization of optical systems based on genetic competition,” in Optical Design and Analysis Software, R. C. Juergens, ed.,  vol. 3780 of Proc. SPIE, 40–47 (1999).
[CrossRef]

L. W. Jones, S. H. Al-Sakran, and J. R. Koza, “Automated synthesis of both the topology and numerical parameters for seven patented optical lens systems using genetic programming,” in Current Developments in Lens Design and Optical Engineering VI, P. Z. Mouroulis, W. J. Smith, and R. B. Johnson, eds.,  vol. 5874 of Proc. SPIE, 587403 (2005).
[CrossRef]

J. P. McGuire, “Designing easily manufactured lenses using a global method,” in International Optical Design Conference 2006, G. G. Gregory, J. M. Howard, and R. J. Koshel, eds.,  vol. 6342 of Proc. SPIE, 63420O (2006).
[CrossRef]

J. R. Rogers, “Using global synthesis to find tolerance-insensitive design forms,” in International Optical Design Conference 2006, G. G. Gregory, J. M. Howard, and R. J. Koshel, eds.,  vol. 6342 of Proc. SPIE, 63420M (2006).
[CrossRef]

G. W. Forbes and A. E. Jones, “Towards global optimization with adaptive simulated annealing,” in 1990 Intl Lens Design Conf, G. N. Lawrence, ed.,  vol. 1354 of Proc. SPIE, 144–153 (1991).
[CrossRef]

T. G. Kuper and T. I. Harris, “Global optimization for lens design - an emerging technology,” in Lens and optical system design, H. Zuegge, ed.,  vol. 1780 of Proc. SPIE, 14–28 (1992).

Science (2)

H. E. Nusse and J. A. Yorke, “Basins of attraction,” Science 271, 1376–1380 (1996).
[CrossRef]

C. Grebogi, E. Ott, and J. A. Yorke, “Chaos, strange attractors, and fractal basin boundaries in non-linear dynamics,” Science 238, 632–638 (1987).
[CrossRef] [PubMed]

SIAM J. on Optimization (1)

I. Castillo and E. R. Barnes, “Chaotic behavior of the affine scaling algorithm for linear programming,” SIAM J. on Optimization 11, 781–795 (2000).
[CrossRef]

Other (7)

B. Davies, “Period Doubling,” in Encyclopedia of Nonlinear Science, A. Scott, ed. (Routledge, New York, 2004).

D. C. Sinclair, “Optical design software,” in Handbook of Optics, Fundamentals, Techniques, and Design, M. Bass, E. W. Van Stryland, D. R. Williams, and Wolfe W. L., eds., vol. 1, 2nd ed., 34.1–34.26 (McGraw-Hill, New York, 1995).

M. Laikin, The method of lens design, Lens design, 4th ed. (CRC Press, Boca Raton, FL, 1996).

H. Gross, H. Zügge, M. Peschka, and F. Blechinger, Principles of optimization, in Handbook of Optical Systems, vol. 3, 291–370 (Wiley-VCH, Weinheim, 2007).

S. N. Rasband, Chaotic dynamics of nonlinear systems (Wiley, New York, 1990).

C. L. Lawson and R. J. Hanson, Solving Least Squares Problems (Prentice-Hall, Englewood Cliffs, NJ, 1974).

E. Ott, Chaos in dynamical systems, 2nd ed. (Cambridge University Press, Cambridge, 2002).

Supplementary Material (1)

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Figures (10)

Fig. 1.
Fig. 1.

Five local minima for a simple two-dimensional monochromatic doublet optimization problem. The control of edge thickness violation was disabled in order to eliminate the constraints from the list of possible causes of the peculiar behavior described in what follows.

Fig. 2.
Fig. 2.

Basins of attraction for the five local minima in Fig. 1, obtained with damping parameter p = 0.002 on a grid of 101 × 101 points. Each grid point is iterated 999 times. The starting point ‘S’ will be used in Sec. 4.

Fig. 3.
Fig. 3.

Period doubling route to chaos. Curvature c 3 of the points in the asymptotic regime for a starting configuration in the basin of doublet local minimum C (see Fig. 2) is shown as function of the damping parameter p.

Fig. 4.
Fig. 4.

Figures on the left: Iteration trajectories in the two-dimensional variable space (c 3,c 2) of a monochromatic doublet for five different values of the damping parameter p. The starting configuration is in the former basin of local minimum C (see Fig. 2). The large gray point corresponds to local minimum C (obtained with sufficient damping), and the iterations shown in gray are considered as initial transients. Figures on the right: The evolution of curvature c 2 as function of the number of iterations. (a) p = 0.0001, (b) p = 0.00009, (c) p = 0.0000854, (d) p = 0.0000833, (e) p = 0.0000777.

Fig. 5.
Fig. 5.

Merit function (MF) values (in relative units) for the points shown in Fig. 3.

Fig. 6.
Fig. 6.

Basins of attraction obtained for different values of the damping parameter p on a grid of 101 × 101 points. Each grid point is iterated 999 times. (a) p = 0.0009, (b) p = 0.00075, (c) p = 0.000679, and (d) p = 0.0006. The black lines starting close to point O in Figs. 6(b) and (c) will be explained in Sec. 5.

Fig. 7.
Fig. 7.

Attracting points for the starting configuration S in the basin of local minimum B (see Fig. 2). The first 300 iterations have been discarded. Curvature c 2 of the iterations between 301 and 999 has been plotted as function of p.

Fig. 8.
Fig. 8.

Figures on the left: Iteration trajectories in the two-dimensional variable space (c 3,c 2) of a monochromatic doublet for five different values of the damping parameter p. The starting configuration is point S in the former basin of local minimum B (see Fig. 2). The large gray point corresponds to local minimum B (obtained with sufficient damping), and the iterations shown in gray are considered as initial transients. Figures on the right: The evolution of curvature c 2 as function of the number of iterations. (a) p = 0.000784, (b) p = 0.000776, (c) p = 0.000768, (d) p = 0.000730, (e) p = 0.000679.

Fig. 9.
Fig. 9.

Merit function (MF) values (in relative units) for the points shown in Fig. 7.

Fig. 10.
Fig. 10.

(Media 1). Iteration history obtained with CODE V data of a set of starting points that are very close to each other. The merit function equimagnitude contours (i.e. the contours along which the merit function remains constant) are shown in gray, and the history of earlier trajectories is shown in green.

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

( J λ k I ) X k = f ,
λ k = p S 1 10 ( k 1 ) / a ,
δ = p 2 p 1 p 3 p 2 4.78

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