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Let $\|n\|$ stand for the integer complexity of the number $n$, i.e. for the least number of $1$'s needed to write $n$ using arbitrary many additions, multiplications, and parentheses. The two-sided inequality $3\log_3 n\leq\|n\|\leq…

Number Theory · Mathematics 2026-05-01 Sergei Konyagin , Kristina Oganesyan

Extending previous analyses on function classes like linear functions, we analyze how the simple (1+1) evolutionary algorithm optimizes pseudo-Boolean functions that are strictly monotone. Contrary to what one would expect, not all of these…

Neural and Evolutionary Computing · Computer Science 2015-03-17 Benjamin Doerr , Thomas Jansen , Dirk Sudholt , Carola Winzen , Christine Zarges

Exhibiting an explicit Boolean function with a large high-order nonlinearity is an important problem in cryptography, coding theory, and computational complexity. We prove lower bounds on the second-order, third-order, and higher-order…

Cryptography and Security · Computer Science 2023-09-21 Jinjie Gao , Haibin Kan , Yuan Li , Jiahua Xu , Qichun Wang

It has long been known that any Boolean function that depends on n input variables has both degree and exact quantum query complexity of Omega(log n), and that this bound is achieved for some functions. In this paper we study the case of…

Quantum Physics · Physics 2013-03-26 Andris Ambainis , Ronald de Wolf

Bernstein-Vazirani algorithm (the one-query algorithm) can identify a completely specified linear Boolean function using a single query to the oracle with certainty. The first aim of the paper is to show that if the provided Boolean…

Quantum Physics · Physics 2015-02-02 Ahmed Younes

Bayesian Optimization (BO) is an effective approach for global optimization of black-box functions when function evaluations are expensive. Most prior works use Gaussian processes to model the black-box function, however, the use of kernels…

Machine Learning · Computer Science 2023-09-25 Dat Phan-Trong , Hung Tran-The , Sunil Gupta

Bayesian optimization (BO) is an effective approach to optimize expensive black-box functions, that seeks to trade-off between exploitation (selecting parameters where the maximum is likely) and exploration (selecting parameters where we…

Machine Learning · Statistics 2021-10-19 Tristan Fauvel , Matthew Chalk

The algebraic degree of Boolean functions (or vectorial Boolean functions) is an important cryptographic parameter that should be computed by fast algorithms. They work in two main ways: (1) by computing the algebraic normal form and then…

Cryptography and Security · Computer Science 2020-07-03 Valentin Bakoev

Inspired by Solomonoffs theory of inductive inference, we propose a prior based on circuit complexity. There are several advantages to this approach. First, it relies on a complexity measure that does not depend on the choice of UTM. There…

Machine Learning · Computer Science 2023-06-27 Cole Wyeth , Carl Sturtivant

We present new algorithms to compute fundamental properties of a Boolean function given in truth-table form. Specifically, we give an O(N^2.322 log N) algorithm for block sensitivity, an O(N^1.585 log N) algorithm for `tree decomposition,'…

Computational Complexity · Computer Science 2007-05-23 Scott Aaronson

We show that almost all n-bit Boolean functions have bounded-error quantum query complexity at least n/2, up to lower-order terms. This improves over an earlier n/4 lower bound of Ambainis, and shows that van Dam's oracle interrogation is…

Quantum Physics · Physics 2012-08-07 Andris Ambainis , Arturs Backurs , Juris Smotrovs , Ronald de Wolf

Bayesian Optimization (BO) is a method for globally optimizing black-box functions. While BO has been successfully applied to many scenarios, developing effective BO algorithms that scale to functions with high-dimensional domains is still…

Machine Learning · Computer Science 2024-02-13 Yihang Shen , Carl Kingsford

The performance and ease of use of deep learning-based binary classifiers have improved significantly in recent years. This has opened up the potential for automating critical inspection tasks, which have traditionally only been trusted to…

Machine Learning · Computer Science 2026-02-25 Thorbjørn Mosekjær Iversen , Zebin Duan , Frederik Hagelskjær

This paper is devoted to the study (common in many applications) of the black-box optimization problem, where the black-box represents a gradient-free oracle $\tilde{f} = f(x) + \xi$ providing the objective function value with some…

Optimization and Control · Mathematics 2024-07-08 Aleksandr Lobanov

A binary string of length $2^k$ induces the Boolean function of $k$ variables whose Shannon expansion is the given binary string. This Boolean function then is representable via a unique reduced ordered binary decision diagram (ROBDD). The…

Information Theory · Computer Science 2011-11-08 J. Kieffer , P. Flajolet , E. -h. Yang

We study the quantum query complexity of the Boolean hidden shift problem. Given oracle access to f(x+s) for a known Boolean function f, the task is to determine the n-bit string s. The quantum query complexity of this problem depends…

Quantum Physics · Physics 2013-11-28 Andrew M. Childs , Robin Kothari , Maris Ozols , Martin Roetteler

Here we consider an approach for fast computing the algebraic degree of Boolean functions. It combines fast computing the ANF (known as ANF transform) and thereafter the algebraic degree by using the weight-lexicographic order (WLO) of the…

Discrete Mathematics · Computer Science 2019-05-22 Valentin Bakoev

The approximate degree of a Boolean function f is the least degree of a real polynomial that approximates f pointwise to error at most 1/3. Approximate degree is known to be a lower bound on quantum query complexity. We resolve or nearly…

Quantum Physics · Physics 2019-08-20 Mark Bun , Robin Kothari , Justin Thaler

We study the optimization of functions with $n>2$ arguments that have a representation as a sum of several functions that have only $2$ of the $n$ arguments each, termed sums of bivariates, on finite domains. The complexity of optimizing…

Optimization and Control · Mathematics 2025-11-26 Nils Müller

The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and engineering. These problems are challenging since the feasible set is typically non-convex…

Machine Learning · Computer Science 2021-03-02 David Eriksson , Matthias Poloczek