Probabilistic and Algebraic Methods in Combinatorics, Geometry, and Theoretical Computer Science

Instructor Arijit Ghosh

Teaching assistant Gopinath Mishra and Subhabrata Paul

Guest speaker Kunal Dutta from Geometrica, INRIA Sophia Antipolis

Description Study applications of probability and algebraic methods in combinatorics, geometry, and theoretical computer science

Prerequisites Undergraduate course in algorithms, discrete mathematics, probability theory and mathematical maturity

References

  • [AS08] N. Alon and J. Spencer, The Probabilistic Method, 2nd ed. Wiley, 2008.

  • [DEG15] K. Dutta, E. Esther and A. Ghosh, Two proofs for Shallow Packings, SoCG 2015. To appear in Discrete & Computational Geometry.

  • [DGJM16] K. Dutta, A. Ghosh, B. Jartoux and N. H. Mustafa, Sets with low shallow cell complexity, Macbeath regions and polynomial partitioning.

  • [DGM16] K. Dutta, A. Ghosh, and N. H. Mustafa, A Simple Proof of Optimal Epsilon Nets, submitted. To appear in Combinatorica.

  • [HP11] S. Har-Peled, Geometric Approximation Algorithms, AMS, 2011. (Preliminary version)

  • [M99] J. Matousek, Geometric Discrepancy: An Illustrated Guide, Springer, 1999.

  • [KMS12] Kaplan, Matousek and Sharir, Simple Proofs of Classical Theorems in Discrete Geometry via the Guth--Katz Polynomial Partitioning Technique, Discrete & Computational Geometry, 2012. (arXiv)

  • [GK10] L. Guth and N. H. Katz, Algebraic methods in discrete analogs of the Kakeya problem, Advances in Mathematics, 2010. (arXiv)

  • [GK15] L. Guth and N. H. Katz, On the Erdos distinct distances problem in the plane, Annals of Mathematics, 2015. (arXiv)

  • [M10] D. Moshkovitz, An Alternative Proof of The Schwartz-Zippel Lemma.

  • [MM10] M. Michalek, A short proof of Combinatorial Nullstellensatz, American Mathematical Monthly, 2010. (arXiv)

  • [CS89] K. L. Clarkson and P. W. Shor, Applications of random sampling in computational geometry, II, Discrete & Computational Geometry, 1989. (Ken's personal copy)

  • [C87] L. Clarkson, New applications of random sampling to computational geometry, Discrete & Computational Geometry, 1987. (Ken's personal copy)

  • [D12] Z. Dvir, Incidence Theorems and Their Applications, Foundations and Trends in Theoretical Computer Science, 2012. (arXiv)

  • [D09] Z. Dvir, On the size of Kakeya sets in finite fields, Journal of the American Mathematical Society, 2009. (arXiv)

  • [H12] N. Harvey, Lecture 9 from "Randomized Algorithms", University of British Columbia, 2012.

  • [E11] D. Ellis, Lecture notes on "The Polynomial Method". From the course Algebraic methods in combinatorics at Cambridge Univ., 2011.

  • [KSS10] H. Kaplan, M. Sharir, and E. Shustin, On lines and Joints, Discrete & Computational Geometry, 2010. (arXiv)

  • [A99] N. Alon, Combinatorial Nullstellensatz, Combinatorics, Probability and Computing, 1999.

  • [AF93] N. Alon and Z. Furedi, Covering the Cube by Affine Hyperplanes, European Journal of Combinatorics, 1993.

  • [J-M-ST] J. Matousek, The Szemerédi–Trotter theorem using polynomials.

  • [M-BU-08] J. Matousek, Using the Borsuk-Ulam Theorem, Springer, 2008.

  • [M16] N. H. Mustafa, A Simple Proof of the Shallow Packing Lemma, Discrete & Computational Geometry, to appear.

  • [S11] A. Sinclair, Lecture 2 from "Randomness & Computation", University of California, Berkeley, 2011.

  • [ST83] E. Szemerédi, W. T. Trotter, Extremal problems in discrete geometry, Combinatorica, 1983.

  • [Sze97] L. A. Szekely, Crossing numbers and hard Erdos problems in discrete geometry, Combinatorics, Probability and Computing, 1997.

  • [Ceg] D. Conlon, Course in Extremal Graph Theory, Univ. Cambridge.

Lecture details

Topics covered in Lecture 1

1. Schwartz-Zippel Lemma: For a proof via induction and probabilistic method refer to [Wiki]. For an algebraic proof using ideas from incidence geometry refer to [M10]. Also read [H12] and [S11].

2. For a proof of Turan's theorem refer to "The Probabilistic Lens: Turan's Theorem" after Chapter 6 from [AS08].

3. Bounding k-sets for a system of disks in the plane via Clarkson-Shor sampling technique refer to the class notes and Section 9.1 of Chapter 9 from [HP11]. Always a good idea to read the original Clarkson-Shor paper [CS89].

Topics covered in Lecture 2

1. Lower bound on crossing number of graphs: Section 9.2 of Chapter 9 from [HP11].

2. Combinatorial Nullstellensatz: Read Sections 1 & 4 from [E11].

3. Also read the papers [A99], [AF93] and [MM10].

Topics covered in Lecture 3

1. Sylvester-Gallai theorem: statement and Kelly's proof. See [Wiki]

2. Vanishing polynomial via linear algebra: Lemma 4 from [E11].

3. Zeev Dvir's proof of "Finite field Kakeya Conjecture": Read [D09] and Section 3 from [E11].

4. Lines and joints problem in 3D: Read Section 3 from [E11]. Also have a look at Section 5 from [GK10] and [KSS10].

Topics covered in Lecture 4

1.Statement of Szemerédi–Trotter Theorem (ST Theorem) bounding incidences $I(P,L)$ between points $P$ and lines $L$ in a plane. See

2. Kovari, Sos and Turan's Theorem (KST Theorem) on excluded $K_{s,t}$-free graphs. See Lecture 8 of [Ceg].

3. Using KST Theorem to get a bound of $O(n^{3/2})$ for the Erdos Unit distance Problem. See Section 2.2.2 from [D12].

4. Using KST Theorem to bound incidences $I(P,L)$ between points $P$ and lines $L$ in a plane. Observe that the bipartite graph obtained from point line incidences is $K_{2,2}$-free.

5. Bounding incidences $I(P,L)$ between points $P$ and lines $L$ in a plane via Cauchy-Schwartz inequality. Read Claim 2.1.2 from Section 2.1 of Chapter 2 from [D12]

6. Szemerédi–Trotter Theorem proof via crossing number lower bound. See Section 2.1.2 of Chapter 2 from [D12] and Section 9.2.2 of Chapter 9 from [HP11].

7. Ham Sandwich Theorem (just statement), Polynomial Ham Sandwich Theorem, and $r$-Partitioning Polynomial method of Guth & Katz. Read Section 2 (all of it) from [KMS12] and Section 2.5 of Chapter 2 from [D12].

Topics covered in Lecture 5

1. Statement of Borsuk-Ulam Theorem, and its use in proving Ham Sandwich Theorems for measures, finite point sets, and general position version. Read Theorem 2.1.1 (just the statement) from Section 2.1 and all of Section 3.1 from [M-BU-08].

2. Proof of Szemerédi–Trotter Theorem using $r$-Partitioning Polynomial method of Guth & Katz. Read Section 3 from [KMS12].

3. Pach-Sharir generalization (just the statement) of Szemerédi–Trotter Theorem for curves. Read Theorem 4.1 from [KMS12].

4. For a set of points P and lines L in the plane. The bound on the number of lines containing more than k points via Szemerédi–Trotter Theorem. See Corollary 2.2.3 from [D12] and Theorem 18.7 from Chapter 18 from Jukna's book on Extremal Combinatorics.

5. Beck's theorem's in combinatorial geometry via Szemerédi–Trotter Theorem. Read Theorem 2.2.4 from [D12].

6. Bound on Erdos unit distance problem via Szemerédi–Trotter Theorem. Read Section 2.2.2 from [D12].

7. Sum product theorem via Szemerédi–Trotter Theorem. Read Section 2.2.3 from [D12].

8. Number of points on a convex curve via Szemerédi–Trotter Theorem. Read Section 2.2.4 from [D12].

Topics covered in Lectures 6, 7, & 8 (by Gopinath Mishra)

1. Fully covered Chapter 5 from [HP11].

Topics covered in Lecture 9

1. Linear-algebraic proof, by Frankl and Pach, of Sauer-Shelah lemma. Read Section 5.2 from [M99].

2. Dudley's weaker version of the Packing Lemma via epsilon-nets, and Chazelle's proof of Haussler's Packing Lemma. Read Section 5.3 from [M99].

Topics covered in Lectures 10 & 11 (Guest lecture by Kunal Dutta)

1. General introduction to VC-dimension, primal shatter dimension, Clarkson-Shor set systems, shallow cell complexity, epsilon-net, and epsilon-sampling theorems. See Sections 5.1, 5.2 and 5.3 from [HP11]. Also read Section 1 from [DGM16].

2. Shallow Packing Lemma statement and proof sketch. See [M16] and [DEG15].

3. Proof of optimal epsilon-net bound via random sampling, Haussler-Welzl's Epsilon-Net Theorem, and shallow packing lemma. See [DGM16].

4. Lower bound of packing number for Clarkson-Shor set systems. See [DGJM16].

5. Definitions of Mnets, and upper bound theorem for Mnets of semi-algebraic set systems in terms of their shallow cell complexity. See [DGJM16].