Daniel Dadush

Office: Room M234
Phone: +31 20 592 4210
Email: dadush AT cwi DOT nl

Announcements:

• I was awarded an ERC Starting grant for the project "Towards a Quantitative Theory of Integer Programming" (see link for the press release). I am now looking for one Postdoc. Please see the official opening here: Postdoc Position. I will review applications starting November 19th, however the application process will remain open until the position are filled.

Events:

• I am a co-organizer for the workshop Lattices: Geometry, Algorithms and Hardness at the Simons Institute, Feb 18-21, 2020.
• I am on the committee of the Mixed Integer Programming (MIP 2019) workshop at MIT, July 11-15, 2019.
• I organized a workshop on Discrepancy Theory and Integer Programming, June 11-15 at CWI. Slides for the talks are available on the website.

Students and Interns:

• Ph.D.:
  • Sander Borst.
  • Samarth Tiwari.
  • Sophie Huiberts.
  • Huck Bennett (joint with Chee Yap). Graduated August 2017. Currently Postdoc @ University of Michigan.
• M.Sc.:
  • Sophie Huiberts. Graduated December 2017.
• Interns:
  • Huck Bennett . Fall 2016.
  • Venkata Gandikota . Fall 2016.

Teaching:

• Fall 2019: Continuous Optimization. Mastermath website.
• Spring 2019: Geometric Functional Analysis and its Applications. Co-Instructor: Jop Briet. Mastermath announcement. Course webpage: link.
• Spring 2018: Introduction to Lattice Algorithms and Cryptography. Co-Instructor: Leo Ducas. Mastermath announcement. Course webpage: link.
• Spring 2017: Advanced Topics in Semidefinite Programming. Co-Instructor: Nikhil Bansal. Mastermath announcement. Course webpage: link.
• Spring 2016: Algorithms Beyond the Worst-Case. Co-Instructor: Bodo Monthey. Mastermath Announcement. Course webpage: link.
• Spring 2013: Lattices, Convexity and Algorithms. Co-Instructor: Oded Regev. New York University (graduate: CSCI-GA 3033-013).

N&O Optimization Seminar:

Research Interests:

• Lattice Algorithms and the Geometry of Numbers
• Linear Programming and the Simplex Method
• Extended Formulations
• Algorithms for Integer Programming, Cutting Plane Methods
• Convex Optimization
• Asymptotic Convex Geometry (properties of convex bodies as dimension tends to infinity)

Research Grants:

• NWO Veni Grant: New Frontiers in Lattice Algorithms and Design, 2015 - 2018.

Research Awards:

• A.W. Tucker Prize for Best Thesis in Mathematical Optimization, 2015. Citation: link.
• Informs Optimization Society Student Paper Prize, 2011.
• Informs JFIG Paper Competition, Finalist, 2010 & 2011.

Research Publications:

Preprints:

1. A Scaling-Invariant Algorithm for Linear Programming whose Running Time Depends only on the Constraint Matrix
   D. Dadush, S. Huiberts, B. Natura, L. Végh. Manuscript, 2019.

Surveys and Book Chapters:

1. Smoothed Analysis of the Simplex Method
   D. Dadush, S. Huiberts. Invited chapter in Beyond Worst Case Analysis (editor Tim Roughgarden), 2020+.

Conference & Journal Publications:

1. On Approximating the Covering Radius and Finding Dense Lattice Subspaces (slides)
   D. Dadush. STOC 2019.
2. AWGN-Goodness is Enough: Capacity-Achieving Lattice Codes based on Dithered Probabilistic Shaping
   A. Campello, D. Dadush, C. Long. IEEE Transactions on Information Theory, Vol. 65(3), 1961 - 1971, 2019.
3. Balancing Vectors in Any Norm
   D. Dadush, S. Nikolov, K. Talwar, N. Tomczak-Jaegermann. FOCS 2018.
4. A Friendly Smoothed Analysis of the Simplex Method
   D. Dadush, S. Huiberts. STOC 2018 (invited to SICOMP special edition).
5. The Gram-Schmidt Walk: A Cure for the Banaszczyk Blues
   N. Bansal, D. Dadush, S. Garg, S. Lovett. Theory of Computing, Vol. 15, Art. 21, 1-27, 2019. Preliminary version in STOC 2018.
6. Lattice based Locality Sensitive Hashing is Optimal (extended abstract)
   C. Chandrasekaran, D. Dadush, E. Grigorescu, V. Gandikota. ITCS 2018.
7. Geometric Rescaling Algorithms for Submodular Function Minimization
   D. Dadush, L. Végh, G. Zambelli. Accepted in Mathematics of Operations Research, 2020. Preliminary version in SODA 2018.
8. Fast, Deterministic and Sparse Dimensionality Reduction
   D. Dadush, C. Guzman, N. Olver. SODA 2018.
9. Rescaling Algorithms for Linear Conic Feasibility
   D. Dadush, L. Végh, G. Zambelli. Accepted in Mathematics of Operations Research, 2019.
10. Towards Strong Reverse Minkowski Type Inequalities for Lattices
    D. Dadush, O. Regev. FOCS 2016.
11. An Algorithm for the Komlós Conjecture Matching Banaszczyk's Bound
    N. Bansal, D. Dadush, S. Garg. SIAM Journal of Computing, Vol. 48(2), 534-553, 2019. Special edition for FOCS 2016.
12. Towards a Constructive Version of Banaszczyk's Vector Balancing Theorem
    D. Dadush, S. Garg, S. Lovett, A. Nikolov. Theory of Computing, Vol. 12, Art. 2, 1-58, 2019. Special edition for APPROX-RANDOM 2016.
13. On the Lattice Distortion Problem
    H. Bennett, D. Dadush, N. Stephens-Davidowitz. ESA 2016.
14. Rescaled Coordinate Descent Methods for Linear Programming
    D. Dadush, L. Végh, G. Zambelli. IPCO 2016.
15. Solving the Closest Vector Problem in 2^n Time: The Discrete Gaussian Strikes Again! (slides)
    D. Aggarwal, D. Dadush, N. Stephens-Davidowitz. FOCS 2015.
16. Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling (slides)
    D. Aggarwal, D. Dadush, O. Regev, N. Stephens-Davidowitz. STOC 2015.
17. Faster Deterministic Volume Estimation in the Oracle Model via Thin Lattice Coverings (slides)
    D. Dadush. SOCG 2015.
18. On the Shadow Simplex Method for Curved Polyhedra (slides)
    D. Dadush, N. Hähnle. Discrete & Computational Geometry, 56, 882-909, 2016.
    Preliminary version in SOCG 2015.
19. Short Paths on the Voronoi Graph and the Closest Vector Problem with Preprocessing (slides)
    N. Bonifas, D. Dadush. SODA 2015.
20. On the Closest Vector Problem with a Distance Guarantee
    D. Dadush, O. Regev, N. Stephens-Davidowitz. CCC 2014.
21. On the existence of 0/1 polytopes with High Semidefinite Extension Complexity (slides)
    J. Briet, D. Dadush, S. Pokutta. Mathematical Programming, Series B, 153, 179-199, 2015.
    Preliminary version in ESA 2013.
22. On the Lattice Smoothing Parameter Problem (slides)
    K.M. Chung, D. Dadush, F.H. Liu, C. Peikert. CCC 2013.
23. Lattice Sparsification and the Approximate Closest Vector Problem (slides)
    D. Dadush, G. Kun. Theory of Computing, Vol. 12, Art. 2, 1-34, 2016. Preliminary version in SODA 2013.
24. Algorithms for the Densest Sublattice Problem
    D. Dadush, D. Micciancio. SODA 2013.
25. Near-Optimal Deterministic Algorithms for Volume Computation via M-Ellipsoids (slides)
    D. Dadush, S. Vempala. Proceedings of the National Academy of Sciences, 110, 19237-19245, 2013.
26. Unconditional Differentially Private Mechanisms for Linear Queries
    A. Bhaskara, D. Dadush, R. Krishnaswamy, K. Talwar. STOC 2012.
27. A Randomized Sieving Algorithm for Approximate Integer Programming (slides)
    D. Dadush. Algorithmica, 70, 208-244, 2014.
    Preliminary version in LATIN 2012.
28. Deterministic Construction of an Approximate M-Ellipsoid and its Applications to Derandomizing Lattice Algorithms
    D. Dadush, S. Vempala. SODA 2012.
29. On the Chvátal-Gomory Closure of a Compact Convex Set (slides)
    D. Dadush, S.S. Dey, J.P. Vielma. Mathematical Programming, Series B, 145, 1, 327-348, 2014.
    Preliminary version in IPCO 2011.
    Optimization Society Student Paper Prize, 2011.
30. The Split Closure of a Strictly Convex Body
    D. Dadush, S.S. Dey, J.P. Vielma. Operations Research Letters, vol. 39, p. 121-126, 2011.
31. The Chvátal-Gomory Closure of a Strictly Convex Body
    D. Dadush, S.S. Dey, J.P. Vielma. Mathematics of Operations Research, vol. 36, p. 227-239, 2011.
    INFORMS JFIG Paper Competition Finalist.
32. Enumerative Lattice Algorithms in Any Norm via M-Ellipsoid Coverings (slides)
    D. Dadush, C. Peikert, S. Vempala. FOCS 2011.
33. Thin Partitions: Isoperimetric Inequalities and a Sampling Algorithm for Star Shaped Bodies
    D. Dadush, C. Chandresekaran, S. Vempala. SODA 2010.

PhD Thesis:

• Integer Programming, Lattice Algorithms, and Deterministic Volume Estimation