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The advantages of quantum random number generators (QRNGs) over pseudo-random number generators (PRNGs) are normally attributed to the nature of quantum measurements. This is often seen as implying the superiority of the sequences of bits…

Quantum Physics · Physics 2019-02-04 Alastair A. Abbott , Cristian S. Calude , Michael J. Dinneen , Nan Huang

Many high-dimensional statistical inference problems are believed to possess inherent computational hardness. Various frameworks have been proposed to give rigorous evidence for such hardness, including lower bounds against restricted…

The paper develops techniques in order to construct computer programs, pseudorandom number generators (PRNG), that produce uniformly distributed sequences. The paper exploits an approach that treats standard processor instructions…

Dynamical Systems · Mathematics 2011-11-15 Vladimir Anashin

In a recent work, Chen, Hoza, Lyu, Tal and Wu (FOCS 2023) showed an improved error reduction framework for the derandomization of regular read-once branching programs (ROBPs). Their result is based on a clever modification to the inverse…

Computational Complexity · Computer Science 2023-12-08 Eshan Chattopadhyay , Jyun-Jie Liao

We construct a pseudorandom generator which fools read-$k$ oblivious branching programs and, more generally, any linear length oblivious branching program, assuming that the sequence according to which the bits are read is known in advance.…

Computational Complexity · Computer Science 2017-08-08 Rohit Gurjar , Ben Lee Volk

We study pseudodeterministic constructions, i.e., randomized algorithms which output the same solution on most computation paths. We establish unconditionally that there is an infinite sequence $\{p_n\}_{n \in \mathbb{N}}$ of increasing…

Computational Complexity · Computer Science 2016-12-07 Igor C. Oliveira , Rahul Santhanam

Cook and Reckhow 1979 pointed out that NP is not closed under complementation iff there is no propositional proof system that admits polynomial size proofs of all tautologies. Theory of proof complexity generators aims at constructing sets…

Computational Complexity · Computer Science 2024-06-12 Jan Krajicek

Emergence of stochastic simulations as an extensively used computational tool for scientific purposes intensified the need for more accurate ways of generating sufficiently long sequences of uncorrelated random numbers. Even though several…

Mathematical Software · Computer Science 2014-08-14 Ayse Ferhan Yesil , M. Cemal Yalabik

Random constraint satisfaction problems (CSPs) such as random $3$-SAT are conjectured to be computationally intractable. The average case hardness of random $3$-SAT and other CSPs has broad and far-reaching implications on problems in…

Computational Complexity · Computer Science 2019-11-11 Jonah Brown-Cohen , Prasad Raghavendra

The planted random subgraph detection conjecture of Abram et al. (TCC 2023) asserts the pseudorandomness of a pair of graphs $(H, G)$, where $G$ is an Erdos-Renyi random graph on $n$ vertices, and $H$ is a random induced subgraph of $G$ on…

Cryptography and Security · Computer Science 2024-09-25 Andrej Bogdanov , Chris Jones , Alon Rosen , Ilias Zadik

We consolidate two widely believed conjectures about tautologies -- no optimal proof system exists, and most require superpolynomial size proofs in any system -- into a $p$-isomorphism-invariant condition satisfied by all paddable…

Computational Complexity · Computer Science 2022-07-21 Hunter Monroe

We study the arithmetic complexity of hitting set generators, which are pseudorandom objects used for derandomization of the polynomial identity testing problem. We give new explicit constructions of hitting set generators whose outputs are…

Computational Complexity · Computer Science 2025-08-19 Robert Andrews

In this paper, we develop invariance-based procedures for testing and inference in high-dimensional regression models. These procedures, also known as randomization tests, provide several important advantages. First, for the global null…

Methodology · Statistics 2023-12-27 Wenxuan Guo , Panos Toulis

Efficient derandomization has long been a goal in complexity theory, and a major recent result by Yanyi Liu and Rafael Pass identifies a new class of hardness assumption under which it is possible to perform time-bounded derandomization…

Computational Complexity · Computer Science 2024-07-01 Yakov Shalunov

We present an efficient algorithm to solve semirandom planted instances of any Boolean constraint satisfaction problem (CSP). The semirandom model is a hybrid between worst-case and average-case input models, where the input is generated by…

Computational Complexity · Computer Science 2023-10-02 Venkatesan Guruswami , Jun-Ting Hsieh , Pravesh K. Kothari , Peter Manohar

We propose algorithms for construction and random generation of hypergraphs without loops and with prescribed degree and dimension sequences. The objective is to provide a starting point for as well as an alternative to Markov chain Monte…

Data Structures and Algorithms · Computer Science 2020-04-14 Naheed Anjum Arafat , Debabrota Basu , Laurent Decreusefond , Stephane Bressan

We describe a new random greedy algorithm for generating regular graphs of high girth: Let $k\geq 3$ and $c \in (0,1)$ be fixed. Let $n \in \mathbb{N}$ be even and set $g = c \log_{k-1} (n)$. Begin with a Hamilton cycle $G$ on $n$ vertices.…

Combinatorics · Mathematics 2020-06-30 Nati Linial , Michael Simkin

Bond rating Transition Probability Matrices (TPMs) are built over a one-year time-frame and for many practical purposes, like the assessment of risk in portfolios or the computation of banking Capital Requirements (e.g. the new IFRS 9…

Risk Management · Quantitative Finance 2017-10-17 Greig Smith , Goncalo dos Reis

Random numbers have significant applications in fundamental science, high-level scientific research, cryptography, and several other areas where there is a pressing need for high-quality random numbers. We present an experimental…

During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has been growing. One of the main reasons for this is that high-probability complexity…

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