The Discrepancy Method

Discrepancy theory is the study of irregularities of distributions. A typical question is: given a "complicated" distribution, find a "simple" one that approximates it well. As it turns out, many questions in complexity theory can be reduced to problems of that type. This raises the possibility that the deep mathematical techniques of discrepancy theory might be of utility to theoretical computer scientists. As will be discussed in this talk this is, indeed, the case. We will give several examples of breakthroughs derived through the application of the "discrepancy method."

[1]  William W. L. Chen On irregularities of distribution. , 1980 .

[2]  Nimrod Megiddo,et al.  Linear Programming in Linear Time When the Dimension Is Fixed , 1984, JACM.

[3]  A. Selman Structure in Complexity Theory , 1986, Lecture Notes in Computer Science.

[4]  W. Schmidt IRREGULARITIES OF DISTRIBUTION (Cambridge Tracts in Mathematics 89) , 1988 .

[5]  Michael Sipser,et al.  Expanders, Randomness, or Time versus Space , 1988, J. Comput. Syst. Sci..

[6]  Russell Impagliazzo,et al.  How to recycle random bits , 1989, 30th Annual Symposium on Foundations of Computer Science.

[7]  Harald Niederreiter,et al.  Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.

[8]  Martin E. Dyer,et al.  A class of convex programs with applications to computational geometry , 1992, SCG '92.

[9]  Alistair Sinclair,et al.  Algorithms for Random Generation and Counting: A Markov Chain Approach , 1993, Progress in Theoretical Computer Science.

[10]  Bernard Chazelle,et al.  On linear-time deterministic algorithms for optimization problems in fixed dimension , 1996, SODA '93.

[11]  Endre Szemerédi,et al.  Constructing Small Sets that are Uniform in Arithmetic Progressions , 1993, Combinatorics, Probability and Computing.

[12]  Bernard Chazelle,et al.  An optimal convex hull algorithm in any fixed dimension , 1993, Discret. Comput. Geom..

[13]  Jiřı́ Matoušek,et al.  Derandomization in Computational Geometry , 1996, J. Algorithms.

[14]  Eyal Kushilevitz,et al.  Communication Complexity: Index of Notation , 1996 .

[15]  Michael Luby,et al.  Pseudorandomness and cryptographic applications , 1996, Princeton computer science notes.

[16]  Bernard Chazelle,et al.  Lower bounds for off-line range searching , 1997, Discret. Comput. Geom..

[17]  Eyal Kushilevitz,et al.  Communication Complexity , 1997, Adv. Comput..

[18]  Robert F. Tichy,et al.  Sequences, Discrepancies and Applications , 1997 .

[19]  Bernard Chazelle A Spectral Approach to Lower Bounds with Applications to Geometric Searching , 1998, SIAM J. Comput..

[20]  Oded Goldreich,et al.  Efficient approximation of product distributions , 1998 .