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ORCIDDaniel Dadush
Dan Dadush
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- affiliation: Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
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2020 – today
- 2024
[j20]Daniel Dadush
, Sophie Huiberts, Bento Natura, László A. Végh:
A scaling-invariant algorithm for linear programming whose running time depends only on the constraint matrix. Math. Program. 204(1): 135-206 (2024)- [j19]Daniel Dadush, Christopher Hojny, Sophie Huiberts, Stefan Weltge:
A simple method for convex optimization in the oracle model. Math. Program. 206(1): 283-304 (2024) - [j18]Daniel Dadush, Zhuan Khye Koh, Bento Natura, László A. Végh:
On circuit diameter bounds via circuit imbalances. Math. Program. 206(1): 631-662 (2024) - [j17]Daniel Dadush, Martin Milanic, Tami Tamir:
Introduction: ACM-SIAM Symposium on Discrete Algorithms (SODA) 2022 Special Issue. ACM Trans. Algorithms 20(3): 21 (2024) - [c46]Daniel Dadush, Akshay Ramachandran:
Strongly Polynomial Frame Scaling to High Precision. SODA 2024: 962-981 - [c45]Daniel Dadush, Zhuan Khye Koh, Bento Natura, Neil Olver, László A. Végh:
A Strongly Polynomial Algorithm for Linear Programs with At Most Two Nonzero Entries per Row or Column. STOC 2024: 1561-1572 - [i38]Daniel Dadush, Akshay Ramachandran:
Strongly Polynomial Frame Scaling to High Precision. CoRR abs/2402.04799 (2024) - 2023
- [j16]Sander Borst, Daniel Dadush, Sophie Huiberts, Samarth Tiwari:
On the integrality gap of binary integer programs with Gaussian data. Math. Program. 197(2): 1221-1263 (2023) - [c44]Sander Borst, Daniel Dadush, Sophie Huiberts, Danish Kashaev:
A Nearly Optimal Randomized Algorithm for Explorable Heap Selection. IPCO 2023: 29-43 - [c43]Daniel Dadush, Friedrich Eisenbrand, Thomas Rothvoss:
From Approximate to Exact Integer Programming. IPCO 2023: 100-114 - [c42]Daniel Dadush, Arthur Léonard, Lars Rohwedder, José Verschae:
Optimizing Low Dimensional Functions over the Integers. IPCO 2023: 115-126 - [c41]Sander Borst, Daniel Dadush, Dan Mikulincer:
Integrality Gaps for Random Integer Programs via Discrepancy. SODA 2023: 1692-1733 - [i37]Daniel Dadush, Arthur Léonard, Lars Rohwedder, José Verschae:
Optimizing Low Dimensional Functions over the Integers. CoRR abs/2303.02474 (2023) - 2022
- [c40]Gilles Bonnet, Daniel Dadush, Uri Grupel, Sophie Huiberts, Galyna Livshyts:
Asymptotic Bounds on the Combinatorial Diameter of Random Polytopes. SoCG 2022: 18:1-18:15 - [c39]Xavier Allamigeon, Daniel Dadush, Georg Loho, Bento Natura, László A. Végh:
Interior point methods are not worse than Simplex. FOCS 2022: 267-277 - [c38]Daniel Dadush, Zhuan Khye Koh, Bento Natura, László A. Végh:
On Circuit Diameter Bounds via Circuit Imbalances. IPCO 2022: 140-153 - [c37]Daniel Dadush, Christopher Hojny, Sophie Huiberts, Stefan Weltge:
A Simple Method for Convex Optimization in the Oracle Model. IPCO 2022: 154-167 - [c36]Daniel Dadush, László A. Végh, Giacomo Zambelli:
On finding exact solutions of linear programs in the oracle model. SODA 2022: 2700-2722 - [c35]Daniel Dadush, Haotian Jiang, Victor Reis:
A new framework for matrix discrepancy: partial coloring bounds via mirror descent. STOC 2022: 649-658 - [i36]Xavier Allamigeon, Daniel Dadush, Georg Loho, Bento Natura, László A. Végh:
Interior point methods are not worse than Simplex. CoRR abs/2206.08810 (2022) - [i35]Sander Borst, Daniel Dadush, Sophie Huiberts, Danish Kashaev:
A nearly optimal randomized algorithm for explorable heap selection. CoRR abs/2210.05982 (2022) - [i34]Daniel Dadush, Friedrich Eisenbrand, Thomas Rothvoss:
From approximate to exact integer programming. CoRR abs/2211.03859 (2022) - 2021
- [j15]Dan Dadush, László A. Végh, Giacomo Zambelli:
Geometric Rescaling Algorithms for Submodular Function Minimization. Math. Oper. Res. 46(3): 1081-1108 (2021) - [c34]Daniel Dadush, Zhuan Khye Koh, Bento Natura, László A. Végh:
An Accelerated Newton-Dinkelbach Method and Its Application to Two Variables per Inequality Systems. ESA 2021: 36:1-36:15 - [c33]Sander Borst, Daniel Dadush, Neil Olver, Makrand Sinha:
Majorizing Measures for the Optimizer. ITCS 2021: 73:1-73:20 - [c32]Sander Borst, Daniel Dadush, Sophie Huiberts, Samarth Tiwari:
On the Integrality Gap of Binary Integer Programs with Gaussian Data. IPCO 2021: 427-442 - [i33]Daniel Dadush, Haotian Jiang, Victor Reis:
A New Framework for Matrix Discrepancy: Partial Coloring Bounds via Mirror Descent. CoRR abs/2111.03171 (2021) - [i32]Daniel Dadush, Zhuan Khye Koh, Bento Natura, László A. Végh:
On Circuit Diameter Bounds via Circuit Imbalances. CoRR abs/2111.07913 (2021) - [i31]Gilles Bonnet, Daniel Dadush, Uri Grupel, Sophie Huiberts, Galyna Livshyts:
Asymptotic Bounds on the Combinatorial Diameter of Random Polytopes. CoRR abs/2112.13027 (2021) - 2020
- [j14]Daniel Dadush, László A. Végh, Giacomo Zambelli:
Rescaling Algorithms for Linear Conic Feasibility. Math. Oper. Res. 45(2): 732-754 (2020) - [j13]Daniel Dadush, Sophie Huiberts:
A Friendly Smoothed Analysis of the Simplex Method. SIAM J. Comput. 49(5) (2020) - [c31]Daniel Dadush, Samarth Tiwari:
On the Complexity of Branching Proofs. CCC 2020: 34:1-34:35 - [c30]Daniel Dadush, Bento Natura, László A. Végh:
Revisiting Tardos's Framework for Linear Programming: Faster Exact Solutions using Approximate Solvers. FOCS 2020: 931-942 - [c29]Daniel Dadush, Sophie Huiberts, Bento Natura, László A. Végh:
A scaling-invariant algorithm for linear programming whose running time depends only on the constraint matrix. STOC 2020: 761-774 - [p1]Daniel Dadush, Sophie Huiberts:
Smoothed Analysis of the Simplex Method. Beyond the Worst-Case Analysis of Algorithms 2020: 309-333 - [i30]Daniel Dadush, Samarth Tiwari:
On the Complexity of Branching Proofs. CoRR abs/2006.04124 (2020) - [i29]Daniel Dadush, Bento Natura, László A. Végh:
Revisiting Tardos's Framework for Linear Programming: Faster Exact Solutions using Approximate Solvers. CoRR abs/2009.04942 (2020) - [i28]Daniel Dadush, Christopher Hojny, Sophie Huiberts, Stefan Weltge:
Simple Iterative Methods for Linear Optimization over Convex Sets. CoRR abs/2011.08557 (2020) - [i27]Sander Borst, Daniel Dadush, Sophie Huiberts, Samarth Tiwari:
On the Integrality Gap of Binary Integer Programs with Gaussian Data. CoRR abs/2012.08346 (2020) - [i26]Sander Borst, Daniel Dadush, Neil Olver, Makrand Sinha:
Majorizing Measures for the Optimizer. CoRR abs/2012.13306 (2020)
2010 – 2019
- 2019
- [j12]Nikhil Bansal, Daniel Dadush, Shashwat Garg:
An Algorithm for Komlós Conjecture Matching Banaszczyk's Bound. SIAM J. Comput. 48(2): 534-553 (2019) - [j11]Antonio Campello, Daniel Dadush, Cong Ling:
AWGN-Goodness Is Enough: Capacity-Achieving Lattice Codes Based on Dithered Probabilistic Shaping. IEEE Trans. Inf. Theory 65(3): 1961-1971 (2019) - [j10]Nikhil Bansal, Daniel Dadush, Shashwat Garg, Shachar Lovett:
The Gram-Schmidt Walk: A Cure for the Banaszczyk Blues. Theory Comput. 15: 1-27 (2019) - [j9]Daniel Dadush, Shashwat Garg, Shachar Lovett, Aleksandar Nikolov:
Towards a Constructive Version of Banaszczyk's Vector Balancing Theorem. Theory Comput. 15: 1-58 (2019) - [c28]Daniel Dadush:
On approximating the covering radius and finding dense lattice subspaces. STOC 2019: 1021-1026 - [i25]Daniel Dadush, Sophie Huiberts, Bento Natura, László A. Végh:
A scaling-invariant algorithm for linear programming whose running time depends only on the constraint matrix. CoRR abs/1912.06252 (2019) - 2018
- [c27]Daniel Dadush, Aleksandar Nikolov, Kunal Talwar, Nicole Tomczak-Jaegermann:
Balancing Vectors in Any Norm. FOCS 2018: 1-10 - [c26]Karthekeyan Chandrasekaran, Daniel Dadush, Venkata Gandikota, Elena Grigorescu:
Lattice-based Locality Sensitive Hashing is Optimal. ITCS 2018: 42:1-42:18 - [c25]Daniel Dadush, László A. Végh, Giacomo Zambelli:
Geometric Rescaling Algorithms for Submodular Function Minimization. SODA 2018: 832-848 - [c24]Daniel Dadush, Cristóbal Guzmán, Neil Olver:
Fast, Deterministic and Sparse Dimensionality Reduction. SODA 2018: 1330-1344 - [c23]Daniel Dadush, Sophie Huiberts:
A friendly smoothed analysis of the simplex method. STOC 2018: 390-403 - [c22]Nikhil Bansal, Daniel Dadush, Shashwat Garg, Shachar Lovett:
The gram-schmidt walk: a cure for the Banaszczyk blues. STOC 2018: 587-597 - 2017
- [i24]Daniel Dadush, László A. Végh, Giacomo Zambelli:
Geometric Rescaling Algorithms for Submodular Function Minimization. CoRR abs/1707.05065 (2017) - [i23]Antonio Campello, Daniel Dadush:
AWGN-Goodness is Enough: Capacity-Achieving Lattice Codes based on Dithered Probabilistic Shaping. CoRR abs/1707.06688 (2017) - [i22]Nikhil Bansal, Daniel Dadush, Shashwat Garg, Shachar Lovett:
The Gram-Schmidt Walk: A Cure for the Banaszczyk Blues. CoRR abs/1708.01079 (2017) - [i21]Daniel Dadush, Sophie Huiberts:
A Friendly Smoothed Analysis of the Simplex Method. CoRR abs/1711.05667 (2017) - [i20]Karthekeyan Chandrasekaran, Daniel Dadush, Venkata Gandikota, Elena Grigorescu:
Lattice-based Locality Sensitive Hashing is Optimal. CoRR abs/1712.08558 (2017) - 2016
- [j8]Daniel Dadush, Nicolai Hähnle:
On the Shadow Simplex Method for Curved Polyhedra. Discret. Comput. Geom. 56(4): 882-909 (2016) - [j7]Daniel Dadush, Gábor Kun:
Lattice Sparsification and the Approximate Closest Vector Problem. Theory Comput. 12(1): 1-34 (2016) - [c21]Daniel Dadush, Shashwat Garg, Shachar Lovett, Aleksandar Nikolov:
Towards a Constructive Version of Banaszczyk's Vector Balancing Theorem. APPROX-RANDOM 2016: 28:1-28:12 - [c20]Huck Bennett, Daniel Dadush, Noah Stephens-Davidowitz:
On the Lattice Distortion Problem. ESA 2016: 9:1-9:17 - [c19]Daniel Dadush, Oded Regev:
Towards Strong Reverse Minkowski-Type Inequalities for Lattices. FOCS 2016: 447-456 - [c18]Nikhil Bansal, Daniel Dadush, Shashwat Garg:
An Algorithm for Komlós Conjecture Matching Banaszczyk's Bound. FOCS 2016: 788-799 - [c17]Daniel Dadush, László A. Végh, Giacomo Zambelli:
Rescaled Coordinate Descent Methods for Linear Programming. IPCO 2016: 26-37 - [i19]Nikhil Bansal, Daniel Dadush, Shashwat Garg:
An Algorithm for Komlós Conjecture Matching Banaszczyk's bound. CoRR abs/1605.02882 (2016) - [i18]Huck Bennett, Daniel Dadush, Noah Stephens-Davidowitz:
On the Lattice Distortion Problem. CoRR abs/1605.03613 (2016) - [i17]Daniel Dadush, Oded Regev:
Towards Strong Reverse Minkowski-type Inequalities for Lattices. CoRR abs/1606.06913 (2016) - [i16]Daniel Dadush, László A. Végh, Giacomo Zambelli:
Rescaling Algorithms for Linear Programming - Part I: Conic feasibility. CoRR abs/1611.06427 (2016) - [i15]Daniel Dadush, Shashwat Garg, Shachar Lovett, Aleksandar Nikolov:
Towards a Constructive Version of Banaszczyk's Vector Balancing Theorem. CoRR abs/1612.04304 (2016) - 2015
- [j6]Jop Briët, Daniel Dadush, Sebastian Pokutta:
On the existence of 0/1 polytopes with high semidefinite extension complexity. Math. Program. 153(1): 179-199 (2015) - [c16]Daniel Dadush, Nicolai Hähnle:
On the Shadow Simplex Method for Curved Polyhedra. SoCG 2015: 345-359 - [c15]Daniel Dadush:
Faster Deterministic Volume Estimation in the Oracle Model via Thin Lattice Coverings. SoCG 2015: 704-718 - [c14]Divesh Aggarwal, Daniel Dadush, Noah Stephens-Davidowitz:
Solving the Closest Vector Problem in 2^n Time - The Discrete Gaussian Strikes Again! FOCS 2015: 563-582 - [c13]Daniel Dadush, Nicolas Bonifas:
Short Paths on the Voronoi Graph and Closest Vector Problem with Preprocessing. SODA 2015: 295-314 - [c12]Divesh Aggarwal, Daniel Dadush, Oded Regev, Noah Stephens-Davidowitz:
Solving the Shortest Vector Problem in 2n Time Using Discrete Gaussian Sampling: Extended Abstract. STOC 2015: 733-742 - [i14]Divesh Aggarwal, Daniel Dadush, Noah Stephens-Davidowitz:
Solving the Closest Vector Problem in $2^n$ Time - The Discrete Gaussian Strikes Again! CoRR abs/1504.01995 (2015) - 2014
- [j5]Daniel Dadush:
A Randomized Sieving Algorithm for Approximate Integer Programming. Algorithmica 70(2): 208-244 (2014) - [j4]Daniel Dadush, Santanu S. Dey, Juan Pablo Vielma:
On the Chvátal-Gomory closure of a compact convex set. Math. Program. 145(1-2): 327-348 (2014) - [c11]Daniel Dadush, Oded Regev, Noah Stephens-Davidowitz:
On the Closest Vector Problem with a Distance Guarantee. CCC 2014: 98-109 - [i13]Daniel Dadush, Oded Regev, Noah Stephens-Davidowitz:
On the Closest Vector Problem with a Distance Guarantee. CoRR abs/1409.8063 (2014) - [i12]Nicolas Bonifas, Daniel Dadush:
Short Paths on the Voronoi Graph and the Closest Vector Problem with Preprocessing. CoRR abs/1412.6168 (2014) - [i11]Daniel Dadush, Nicolai Hähnle:
On the Shadow Simplex Method for Curved Polyhedra. CoRR abs/1412.6705 (2014) - [i10]Kai-Min Chung, Daniel Dadush, Feng-Hao Liu, Chris Peikert:
On the Lattice Smoothing Parameter Problem. CoRR abs/1412.7979 (2014) - [i9]Divesh Aggarwal, Daniel Dadush, Oded Regev, Noah Stephens-Davidowitz:
Solving the Shortest Vector Problem in $2^n$ Time via Discrete Gaussian Sampling. CoRR abs/1412.7994 (2014) - 2013
- [j3]Daniel Dadush, Santosh S. Vempala:
Near-optimal deterministic algorithms for volume computation via M-ellipsoids. Proc. Natl. Acad. Sci. USA 110(48): 19237-19245 (2013) - [c10]Kai-Min Chung, Daniel Dadush, Feng-Hao Liu, Chris Peikert:
On the Lattice Smoothing Parameter Problem. CCC 2013: 230-241 - [c9]Jop Briët, Daniel Dadush, Sebastian Pokutta:
On the Existence of 0/1 Polytopes with High Semidefinite Extension Complexity. ESA 2013: 217-228 - [c8]Daniel Dadush, Gábor Kun:
Lattice Sparsification and the Approximate Closest Vector Problem. SODA 2013: 1088-1102 - [c7]Daniel Dadush, Daniele Micciancio:
Algorithms for the Densest Sub-Lattice Problem. SODA 2013: 1103-1122 - [i8]Jop Briët, Daniel Dadush, Sebastian Pokutta:
On the existence of 0/1 polytopes with high semidefinite extension complexity. CoRR abs/1305.3268 (2013) - [i7]Daniel Dadush:
A Deterministic Polynomial Space Construction for eps-nets under any Norm. CoRR abs/1311.6671 (2013) - 2012
- [c6]Daniel Dadush:
A O(1/ε 2) n -Time Sieving Algorithm for Approximate Integer Programming. LATIN 2012: 207-218 - [c5]Daniel Dadush, Santosh S. Vempala:
Deterministic construction of an approximate M-ellipsoid and its applications to derandomizing lattice algorithms. SODA 2012: 1445-1456 - [c4]Aditya Bhaskara, Daniel Dadush, Ravishankar Krishnaswamy, Kunal Talwar:
Unconditional differentially private mechanisms for linear queries. STOC 2012: 1269-1284 - [i6]Daniel Dadush, Santosh S. Vempala:
Deterministic 2^{O(n)} Algorithms for M-Ellipsoids, Lattice Problems and Volume Estimation. CoRR abs/1201.5972 (2012) - [i5]Daniel Dadush, Gábor Kun:
Lattice Sparsification and the Approximate Closest Vector Problem. CoRR abs/1212.6781 (2012) - 2011
- [j2]Daniel Dadush, Santanu S. Dey, Juan Pablo Vielma:
The Chvátal-Gomory Closure of a Strictly Convex Body. Math. Oper. Res. 36(2): 227-239 (2011) - [j1]Daniel Dadush, Santanu S. Dey, Juan Pablo Vielma:
The split closure of a strictly convex body. Oper. Res. Lett. 39(2): 121-126 (2011) - [c3]Daniel Dadush, Chris Peikert, Santosh S. Vempala:
Enumerative Lattice Algorithms in any Norm Via M-ellipsoid Coverings. FOCS 2011: 580-589 - [c2]Daniel Dadush, Santanu S. Dey, Juan Pablo Vielma:
On the Chvátal-Gomory Closure of a Compact Convex Set. IPCO 2011: 130-142 - [i4]Daniel Dadush, Santosh S. Vempala:
Deterministic Construction of an Approximate M-Ellipsoid and its Application to Derandomizing Lattice Algorithms. CoRR abs/1107.5478 (2011) - [i3]Daniel Dadush:
A O(1/eps^2)^n Time Sieving Algorithm for Approximate Integer Programming. CoRR abs/1109.2477 (2011) - 2010
- [c1]Karthekeyan Chandrasekaran, Daniel Dadush, Santosh S. Vempala:
Thin Partitions: Isoperimetric Inequalities and a Sampling Algorithm for Star Shaped Bodies. SODA 2010: 1630-1645 - [i2]Daniel Dadush, Chris Peikert, Santosh S. Vempala:
Enumerative Algorithms for the Shortest and Closest Lattice Vector Problems in Any Norm via M-Ellipsoid Coverings. CoRR abs/1011.5666 (2010)
2000 – 2009
- 2009
- [i1]Karthekeyan Chandrasekaran, Daniel Dadush, Santosh S. Vempala:
Thin Partitions: Isoperimetric Inequalities and Sampling Algorithms for some Nonconvex Families. CoRR abs/0904.0583 (2009)
Coauthor Index
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