Reconstruction of integers from pairwise distances

Given a set of integers, one can easily construct the set of their pairwise distances. We consider the inverse problem: given a set of pairwise distances, find the integer set which realizes the pairwise distance set. This problem arises in a lot of fields in engineering and applied physics, and has confounded researchers for over 60 years. It is one of the few fundamental problems that are neither known to be NP-hard nor solvable by polynomial-time algorithms. Whether unique recovery is possible also remains an open question. In many practical applications where this problem occurs, the integer set is naturally sparse (i.e., the integers are sufficiently spaced), a property which has not been explored. In this work, we exploit the sparse nature of the integer set and develop a polynomial-time algorithm which provably recovers the set of integers (up to linear shift and reversal) from the set of their pairwise distances with arbitrarily high probability if the sparsity is O(n1/2-ε). Numerical simulations verify the effectiveness of the proposed algorithm.

[1]  A. Walther The Question of Phase Retrieval in Optics , 1963 .

[2]  J R Fienup,et al.  Phase retrieval algorithms: a comparison. , 1982, Applied optics.

[3]  James R. Fienup,et al.  Reconstruction of the support of an object from the support of its autocorrelation , 1982 .

[4]  Martin Vetterli,et al.  Sparse spectral factorization: Unicity and reconstruction algorithms , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  øöö Blockinø Phase retrieval, error reduction algorithm, and Fienup variants: A view from convex optimization , 2002 .

[6]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[7]  Steven Skiena,et al.  Reconstructing sets from interpoint distances (extended abstract) , 1990, SCG '90.

[8]  J R Fienup,et al.  Reconstruction of an object from the modulus of its Fourier transform. , 1978, Optics letters.

[9]  Yonina C. Eldar,et al.  Sparsity Based Sub-wavelength Imaging with Partially Incoherent Light via Quadratic Compressed Sensing References and Links , 2022 .

[10]  Peter G. Casazza,et al.  Phase retrieval , 2015, SPIE Optical Engineering + Applications.

[11]  Mark Stefik,et al.  Inferring DNA Structures from Segmentation Data , 1978, Artif. Intell..

[12]  J. Franklin,et al.  Ambiguities in the X‐ray analysis of crystal structures , 1974 .

[13]  Babak Hassibi,et al.  On robust phase retrieval for sparse signals , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[14]  A. L. Patterson A Direct Method for the Determination of the Components of Interatomic Distances in Crystals , 1935 .

[15]  R. Gerchberg A practical algorithm for the determination of phase from image and diffraction plane pictures , 1972 .

[16]  Ramesh Hariharan,et al.  The restriction mapping problem revisited , 2002, J. Comput. Syst. Sci..

[17]  Heinz H. Bauschke,et al.  Phase retrieval, error reduction algorithm, and Fienup variants: a view from convex optimization. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  M. Shamos Problems in computational geometry , 1975 .

[19]  Rick P. Millane,et al.  Phase retrieval in crystallography and optics , 1990 .

[20]  Arvind Gupta,et al.  On the turnpike problem , 2000 .

[21]  Lee Aaron Newberg,et al.  An algorithm for analysing probed partial digestion experiments , 1995, Comput. Appl. Biosci..

[22]  Babak Hassibi,et al.  Recovery of sparse 1-D signals from the magnitudes of their Fourier transform , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.