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We prove that any random variable $X$ whose moment generating function is point-wise upper bounded by that of $ G \sim \mathcal{N}(0,1) $ must be dominated by $ G/\mathbb{E}[|G|] $ in convex order, meaning $ \mathbb{E}[f(X)] \le…

Probability · Mathematics 2026-04-30 Yihan Zhang

In this paper, we present a more complete version of the minimax theorem established in [7]. As a consequence, we get, for instance, the following result: Let $X$ be a compact, not singleton subset of a normed space $(E,\|\cdot\|)$ and let…

Functional Analysis · Mathematics 2021-04-13 Biagio Ricceri

Freedman's inequality is a martingale counterpart to Bernstein's inequality. This result shows that the large-deviation behavior of a martingale is controlled by the predictable quadratic variation and a uniform upper bound for the…

Probability · Mathematics 2015-03-17 Joel A. Tropp

One problem of wide interest involves estimating expected crossing-times. Several tools have been developed to solve this problem beginning with the works of Wald and the theory of sequential analysis. An extension of his approach is…

Methodology · Statistics 2015-06-17 Mark Brown , Victor de la Pena , Tony Sit

A general achievable upper bound of extractable work under feedback control is given, where nonequilibrium equalities are generalized so as to be applicable to error-free measurements. The upper bound involves a term which arises from the…

Statistical Mechanics · Physics 2014-11-20 Yuto Ashida , Ken Funo , Yûto Murashita , Masahito Ueda

We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies. We derive new tighter deterministic and probabilistic bounds that we use to…

Optimization and Control · Mathematics 2022-03-07 Anis Hamadouche , Yun Wu , Andrew M. Wallace , Joao F. C. Mota

Based on the stochastic maximum principle for the partially coupled forward-backward stochastic control system (FBSCS for short), a modified method of successive approximations (MSA for short) is established for stochastic recursive optimal…

Optimization and Control · Mathematics 2022-01-11 Shaolin Ji , Rundong Xu

This paper presents some limit theorems for certain functionals of moving averages of semimartingales plus noise which are observed at high frequency. Our method generalizes the pre-averaging approach (see [Bernoulli 15 (2009) 634--658,…

Statistics Theory · Mathematics 2010-10-05 Jean Jacod , Mark Podolskij , Mathias Vetter

We consider the problem of adaptive estimation of the regression function in a framework where we replace ergodicity assumptions (such as independence or mixing) by another structural assumption on the model. Namely, we propose adaptive…

Statistics Theory · Mathematics 2010-11-03 Sylvain Delattre , Stéphane Gaïffas

Building on the free-probability stochastic control framework introduced in arXiv:2502.17329, we connect optimal control problems for $n \times n$ random matrix ensembles with their infinite-dimensional, free-probability analogues. Under…

Analysis of PDEs · Mathematics 2025-12-01 Wilfrid Gangbo , David Jekel , Kyeongsik Nam , Aaron Z. Palmer

Convergence of Extremum Seeking (ES) algorithms has been established in the limit of small gains. Using averaging theory and contraction analysis, we propose a framework for computing explicit bounds on the departure of the ES scheme from…

Optimization and Control · Mathematics 2013-03-20 Gabriel Bousquet , Jean-Jacques Slotine

In this paper we study processes which are constructed by a convolution of a deterministic kernel with a martingale. A special emphasis is put on the case where the driving martingale is a centred L\'evy process, which covers the popular…

Probability · Mathematics 2021-05-31 Christian Bender , Robert Knobloch , Philip Oberacker

We investigate when the exponential sum $S_f(x,\alpha) := \sum_{n\le x}f(n)\mathrm{e}(n\alpha)$ is bounded, for a multiplicative function $f$ and $\alpha\in\mathbb{R}$. We show that under natural assumptions, $S_f(x,\alpha)$ is bounded only…

Number Theory · Mathematics 2026-02-24 Pierre-Alexandre Bazin , Ihor Pylaiev , Fred Tyrrell

We study the performance of stochastic first-order methods for finding saddle points of convex-concave functions. A notorious challenge faced by such methods is that the gradients can grow arbitrarily large during optimization, which may…

Machine Learning · Computer Science 2024-06-10 Gergely Neu , Nneka Okolo

This paper concerns the estimation of sums of functions of observable and unobservable variables. Lower bounds for the asymptotic variance and a convolution theorem are derived in general finite- and infinite-dimensional models. An explicit…

Statistics Theory · Mathematics 2007-06-13 Cun-Hui Zhang

In this paper, we consider a class of stochastic optimal control problems with risk constraints that are expressed as bounded probabilities of failure for particular initial states. We present here a martingale approach that diffuses a risk…

Systems and Control · Computer Science 2015-07-09 Vu Anh Huynh , Leonid Kogan , Emilio Frazzoli

We show that the big-O problem for max-plus automata is decidable and PSPACE-complete. The big-O (or affine domination) problem asks whether, given two max-plus automata computing functions f and g, there exists a constant c such that f <…

Formal Languages and Automata Theory · Computer Science 2025-07-16 Laure Daviaud , David Purser , Marie Tcheng

Sign changes in sums of arithmetic functions and their inverses are a subtle topic with room to grow new results. Suppose that $S_f(x) := \sum_{n \leq x} f(n)$ is the summatory function of some arithmetic function $f$ such that $f(1) \neq…

Number Theory · Mathematics 2026-03-10 Maxie Dion Schmidt

The first passage time process of a L\'evy subordinator with heavy-tailed L\'evy measure has long-range dependent paths. The random fluctuations that appear under two natural schemes of summation and time scaling of such stochastic…

Probability · Mathematics 2012-04-02 Ingemar Kaj , Anders Martin-Löf

We study learning problems involving arbitrary classes of functions $F$, distributions $X$ and targets $Y$. Because proper learning procedures, i.e., procedures that are only allowed to select functions in $F$, tend to perform poorly unless…

Machine Learning · Statistics 2018-04-17 Shahar Mendelson