Machine Learning

Every time a streaming service guesses what you would rate a film you have never seen, it is solving a matrix completion problem: fill in the missing entries of a vast user-by-item table from the few that are observed. The question became famous during the Netflix Prize (2006-2009), and it looks hopeless - infinitely many matrices fit the observed entries - until one assumes the structure that makes recommendation possible: the table is essentially low rank, because tastes are governed by a few latent factors. In their landmark 2009 paper 'Exact Matrix Completion via Convex Optimization' (Foundations of Computational Mathematics), Emmanuel Candes and Benjamin Recht proved that an n-by-n matrix of rank r can be recovered exactly, with high probability, from only about n^1.2 * r * log n randomly observed entries - not by the NP-hard route of minimizing rank, but by minimizing the nuclear norm, a convex surrogate (the sum of the singular values) solvable efficiently. The proof, in the lineage of Candes-Romberg-Tao compressed sensing, turns on two ideas: an incoherence condition ensuring the singular vectors are spread out rather than spiky, and a dual certificate witnessing optimality, whose existence rests on delicate random-matrix concentration. It transformed a practical engineering puzzle into rigorous theory and seeded a decade of work across machine learning, signal processing, computer vision, and sensor localization. This mission formalizes the Candes-Recht exact-recovery theorem in Lean, decomposed into its dual-certificate construction and the probabilistic concentration reductions at its core.

The basic UCB regret bound of Mission II is logarithmic but not tight: its leading constant $16/\Delta_i$ is eight times the information-theoretic limit, and its worst-case rate carries a spurious $\sqrt{\log n}$. Chapters 8–9 of Lattimore–Szepesvári close both gaps. A refined confidence schedule $f(t) = 1 + t\log^2 t$ yields the asymptotically optimal $\limsup_{n\to\infty} R_n/\log n \le \sum_{i:\Delta_i>0} 2/\Delta_i$ — exactly matching the instance-dependent lower bound of Mission VII for Gaussian noise. The MOSS index $\hat\mu_i + \sqrt{\tfrac{4}{T_i}\log^+\!\big(\tfrac{n}{k T_i}\big)}$ achieves minimax regret $R_n \le 39\sqrt{kn} + \sum_i \Delta_i$, matching the $\Omega(\sqrt{kn})$ lower bound up to a constant. These two theorems are the gold standard for finite-armed stochastic bandits.

A learner repeatedly chooses one of $k$ slot machines, observes only the reward of the chosen arm, and wants to earn almost as much as the best arm in hindsight. This is the stochastic multi-armed bandit, the canonical model of the exploration–exploitation dilemma. This mission formalizes the model (environments, policies, regret, and the regret decomposition $R_n = \sum_i \Delta_i\,\mathbb{E}[T_i(n)]$) and the two classical algorithms of Chapters 6–7 of Lattimore–Szepesvári: Explore-Then-Commit and the Upper Confidence Bound algorithm built on the optimism principle. The goal theorem is the instance-dependent UCB regret bound $R_n \le 3\sum_i \Delta_i + \sum_{i:\Delta_i>0} 16\log(n)/\Delta_i$ — logarithmic regret with explicit constants, the single most cited result of bandit theory — together with its distribution-free companion $R_n \le 8\sqrt{nk\log n} + 3\sum_i \Delta_i$.

The oldest bandit algorithm (Thompson, 1933) is also the most modern: sample a parameter from the posterior and act greedily. Chapters 34–36 of Lattimore–Szepesvári develop the Bayesian view in two crowning results. The Gittins index theorem: for infinite-horizon discounted Markov bandits, the seemingly intractable dynamic program is solved exactly by an index policy — each arm gets a retirement-value index computable arm-by-arm, and playing the largest index is Bayesian optimal. And the frequentist analysis of Thompson sampling — the goal theorem: with Gaussian posteriors, Thompson sampling on 1-subgaussian bandits achieves $\lim_{n\to\infty} R_n/\log n = \sum_{i:\Delta_i>0} 2/\Delta_i$, exactly asymptotically optimal, alongside the minimax-grade $R_n \le C\sqrt{kn\log n}$. Together they explain why posterior sampling is both principled and practically dominant.

Is the $d\sqrt{n}$ regret of LinUCB (Mission X) an artifact of the algorithm or a law of nature? Chapters 24–25 of Lattimore–Szepesvári prove it is essentially unimprovable. On the unit ball there is a parameter $\theta$ with $\|\theta\|_2^2 = d^2/(48n)$ forcing $R_n \ge \frac{d\sqrt{n}}{16\sqrt{3}}$ — the goal theorem — and the hypercube gives the same $\Omega(d\sqrt{n})$ rate. The asymptotic chapter is more striking still: for fixed finite action sets, the instance-optimal constant $c(\mathcal{A},\theta)$ is characterized by an allocation program, and optimism itself is provably suboptimal — LinUCB and Thompson sampling cannot achieve it, because exploration must sometimes deliberately play actions optimism would never touch. These lower bounds define the targets for the entire linear-bandit literature.

What if the rewards are not random at all, but chosen by an adversary who knows your algorithm? Remarkably, a randomized learner can still compete with the best fixed arm in hindsight. Chapters 11–12 of Lattimore–Szepesvári develop the adversarial $k$-armed bandit: rewards $x_{ti} \in [0,1]$ are an arbitrary fixed matrix, the learner samples $A_t \sim P_t$, and regret is measured against $\max_i \sum_t x_{ti}$. The exponential-weights algorithm Exp3, fed by importance-weighted loss estimates $\hat X_{ti} = 1 - \mathbb{1}\{A_t = i\}(1 - X_t)/P_{ti}$, achieves $R_n \le \sqrt{2nk\log k}$ — the goal theorem. The companion Exp3-IX, which deliberately biases its estimator, upgrades this to a bound holding with high probability rather than only in expectation. These results are the foundation of all adversarial online learning with partial feedback.

Motivation

In a linear stochastic bandit, a learner repeatedly chooses an action from a set of vectors and receives a noisy reward whose mean is linear in the action. The model underlies contextual recommendation, adaptive routing and dynamic pricing, where each option is described by features and the payoff of a feature vector must be learned while it is exploited. The quality of a strategy is measured by its regret: the reward lost, relative to always playing the best action, over the first $n$ rounds.

The optimism-in-the-face-of-uncertainty principle (play as if the most favourable parameter consistent with the data were true) was introduced for linear bandits by Auer (2002), and developed by Dani, Hayes and Kakade (2008) (ConfidenceBall, regret $O(d\sqrt n\log^{3/2} n)$ with confidence sets from a union bound over time) and Rusmevichientong and Tsitsiklis (2010). Abbasi-Yadkori, Pál and Szepesvári (NIPS 2011) replaced the union bound by a self-normalized martingale inequality that holds uniformly in time. It gives smaller confidence ellipsoids and, through them, a high-probability regret bound for the resulting algorithm, OFUL, that improves the earlier ones by logarithmic factors. The inequality became the standard tool for linear and kernelized bandits and for linear reinforcement learning.

Setting

Fix a dimension $d \ge 1$ and an unknown parameter $\theta_* \in \mathbb R^d$. In round $t = 1, 2, \dots$ the learner is given a nonempty decision set $D_t \subseteq \mathbb R^d$, chooses $X_t \in D_t$, and observes the reward

There is a filtration $\{F_t\}_{t \ge 0}$ such that $X_t$ is $F_{t-1}$-measurable and $\eta_t$ is $F_t$-measurable and conditionally $R$-sub-Gaussian: $\mathbf E[e^{\lambda\eta_t} \mid F_{t-1}] \le \exp(\lambda^2R^2/2)$ for all $\lambda \in \mathbb R$, with $R \ge 0$ fixed.

For a regularization parameter $\lambda > 0$ let $\overline V_t = \lambda I + \sum_{s=1}^t X_sX_s^\top$ and let $\widehat\theta_t = \overline V_t^{-1}\sum_{s=1}^t Y_sX_s$ be the regularized least-squares estimate. With $\|v\|_A = \sqrt{v^\top A v}$ and a known bound $\|\theta_*\|_2 \le S$, the confidence ellipsoid is

The OFUL algorithm chooses, in round $t$, a pair $(X_t, \widetilde\theta_t)$ maximizing $\langle x, \theta \rangle$ over $D_t \times C_{t-1}$. The pseudo-regret is $R_n = \sum_{t=1}^n \langle x^*_t - X_t, \theta_* \rangle$, where $\langle x^*_t, \theta_*\rangle = \max_{x\in D_t}\langle x,\theta_*\rangle$.

Formalization targets

Goal: Theorem 3, the regret of OFUL

If $\|X_t\|_2 \le L$, $\langle x, \theta_*\rangle \in [-1,1]$ for all $x \in D_t$, and $\lambda \ge \max(1, L^2)$, then for every $\delta > 0$, with probability at least $1 - \delta$,

Milestone: Theorem 1, the self-normalized bound

For any positive definite $V$, $\overline V_t = V + \sum_{s\le t}X_sX_s^\top$ and $S_t = \sum_{s \le t}\eta_sX_s$: with probability at least $1-\delta$, for all $t \ge 0$,

Milestones: Theorem 2, the confidence ellipsoids

With probability at least $1 - \delta$, $\theta_* \in C_t$ for all $t \ge 0$ (first claim). If $\|X_t\|_2 \le L$, then with probability at least $1-\delta$, for all $t$, $\|\widehat\theta_t - \theta_*\|_{\overline V_t} \le R\sqrt{d\log((1 + tL^2/\lambda)/\delta)} + \lambda^{1/2}S$ (second claim, stated here for $d \ge 2$).

Significance

Theorem 3 bounds the regret of OFUL by $O(d\sqrt n\log n)$ with high probability, uniformly over the horizon, so it holds for an unknown horizon without restarting. The bound applies to arbitrary, even adversarially changing, decision sets. Theorem 1 is the ingredient that makes this possible: a deviation bound for a vector-valued martingale, normalized by its own random covariance, that holds for all times simultaneously and whose logarithmic term is a determinant rather than a union-bound count. The same inequality underlies regret analyses of generalized linear bandits, kernelized bandits, linear Markov decision processes and many confidence-sequence constructions.

All three results are proved in the paper's appendices (not included in the source file used here). None of them is formalized in the stated generality. Prove2Me holds the special cases $V = \lambda I$, $R = 1$, $\delta < 1$ of Theorems 1 and 2 (from the Bandit Algorithms textbook series), a pathwise LinUCB regret lemma that assumes the confidence event, and the elliptical potential lemma. A formal proof of Theorem 3 would be the first machine-checked high-probability regret bound for OFUL with the paper's confidence sets.

Difficulty

The actions are chosen adaptively, by an argmax over a data-dependent set, so the sequence $X_t$ has no independence structure and the least-squares estimate is not a sum of independent terms. A fixed-design concentration bound followed by a union bound over time and over a covering of the sphere loses logarithmic factors and does not produce the determinant in the radius; that is the route of the earlier work that Theorem 1 improves. Theorem 1 must hold for all times at once for a quantity normalized by the random matrix $\overline V_t$, which is itself built from the adaptively chosen actions; a bound for each fixed $t$ does not give it.

Formalization scope

Vectors are Fin d → ℝ, matrices Matrix (Fin d) (Fin d) ℝ, and $\|x\|_A$ is Real.sqrt (x ⬝ᵥ A *ᵥ x). Rounds are indexed $t+1$ for $t : ℕ$, so sums over $s \le t$ are sums over Finset.range t at index s + 1, and the time-0 objects are empty sums. The probability space is standard Borel, as Mathlib's conditional sub-Gaussianity (HasCondSubgaussianMGF, variance proxy $R^2$) requires; this is an added hypothesis. Every "with probability at least $1-\delta$, for all $t$" is stated as an outer-measure bound $\le \delta$ on the failure event, with the time quantifier inside the event. $\det(\cdot)^{1/2}$ is the real square root of the determinant; the matrices inverted are positive definite, so Lean's junk inverse never occurs.

OFUL is a predicate on the whole process: in every round the chosen pair maximizes $\langle x,\theta\rangle$ over $D_t \times C_{t-1}$, with any tie-breaking. Runs exist whenever the decision sets are nonempty and compact. The measurability of the actions is assumed, as in Theorem 1. The optimal reward $\langle x^*_t, \theta_*\rangle$ is the supremum over $D_t$, finite because of the reward bound.

Two corrections to the printed Theorem 3 are made and disclosed. The printed $nL/d$ is replaced by $nL^2/d$, which is what the determinant–trace bound $\det\overline V_n \le (\lambda + nL^2/d)^d$ gives; for $L \le 1$ the corrected bound implies the printed one. The hypothesis $\lambda \ge \max(1, L^2)$ is added: for $\lambda < 1$ the printed logarithm can be negative, and the printed bound would then assert $R_n \le 0$. In the second claim of Theorem 2, $d \ge 2$ is added, because at $d = 1$ the claim does not follow from the first claim and the paper's proof is not available.

The goal is a probability bound over the noise, not the pathwise statement "if $\theta_* \in C_{t-1}$ for all $t$ then $R_n \le \dots$". The pathwise statement assumes the confidence event instead of proving it, and is already on the platform. The confidence sets inside the OFUL predicate use the same $\delta$ as the conclusion.

Needed infrastructure: maximal inequalities for nonnegative supermartingales, Gaussian integrals of quadratic forms on $\mathbb R^d$, log-determinant bounds for sums of rank-one updates, and the determinant–trace inequality. The platform rows BanditAlgorithm.self_normalized_martingale_bound, BanditAlgorithm.least_squares_confidence_ellipsoid and BanditAlgorithm.elliptical_potential_lemma are referenced as tools. Proofs of the milestones, generalizations of the existing special cases to general $V$ and $R$, and reusable determinant lemmas are all welcome.

Selected references

• Y. Abbasi-Yadkori, D. Pál, Cs. Szepesvári, Improved Algorithms for Linear Stochastic Bandits, Advances in Neural Information Processing Systems 24 (NIPS), 2011. https://proceedings.neurips.cc/paper/2011
• P. Auer, Using Confidence Bounds for Exploitation-Exploration Trade-offs, Journal of Machine Learning Research 3, 2002. https://www.jmlr.org/papers/v3/auer02a.html
• V. Dani, T. P. Hayes, S. M. Kakade, Stochastic Linear Optimization under Bandit Feedback, COLT, 2008. http://colt2008.cs.helsinki.fi/papers/80-Dani.pdf
• P. Rusmevichientong, J. N. Tsitsiklis, Linearly Parameterized Bandits, Mathematics of Operations Research 35(2), 2010. https://doi.org/10.1287/moor.1100.0446
• T. Lattimore, Cs. Szepesvári, Bandit Algorithms, Cambridge University Press, 2020, Chapters 19–20. https://doi.org/10.1017/9781108571401

The final step from bandits to reinforcement learning: actions now change the state of the world. Chapter 38 of Lattimore–Szepesvári studies online learning in an unknown Markov decision process with $S$ states, $A$ actions and rewards in $[0,1]$. The optimism principle of Mission II scales up: UCRL2 maintains confidence sets over transition kernels, solves an extended MDP by extended value iteration, and recomputes only when a state-action count doubles. The goal theorem: with probability $1-\delta$, $\hat R_n < C\,D(M)\,S\sqrt{An\log(nSA/\delta)}$, where $D(M)$ is the diameter of the MDP — sublinear regret with no prior knowledge of the dynamics. The matching lower bound $\mathbb{E}[\hat R_n] \ge C'\sqrt{DSAn}$ brackets the true complexity of tabular reinforcement learning up to $\sqrt{DS}$.

How well can any algorithm possibly do? Chapters 13–17 of Lattimore–Szepesvári answer with three matching impossibility results. The divergence decomposition identifies the information a policy collects: $D(\mathbb{P}_{\nu\pi}, \mathbb{P}_{\nu'\pi}) = \sum_i \mathbb{E}[T_i(n)] D(P_i, P_i')$. Feeding it into the Bretagnolle–Huber inequality of Mission VI yields the goal theorem — the minimax lower bound $R_n \ge \frac{1}{27}\sqrt{(k-1)n}$ over Gaussian bandits, showing MOSS (Mission III) is optimal up to a constant. The same machinery gives the instance-dependent bound of Lai–Robbins type: every consistent policy suffers $\liminf_n R_n/\log n \ge \sum_{i:\Delta_i>0} \Delta_i / d_{\inf}(P_i, \mu^*, \mathcal{M}_i)$, certifying the asymptotic optimality of the UCB of Mission III and KL-UCB of Mission IV, and a high-probability lower bound showing the Exp3-IX guarantees of Mission V cannot be improved.

Motivation

In best arm identification with fixed confidence, a learner samples $K$ unknown distributions (arms) sequentially and must, as early as possible, name the arm with the largest mean, while being wrong with probability at most a prescribed risk $\delta$. The problem models adaptive A/B/n testing, clinical and simulation-based selection among alternatives, and the "ranking and selection" problem of operations research and simulation optimization. The quantity of interest is the sample complexity $\mathbb E_{\boldsymbol\mu}[\tau_\delta]$, the expected number of samples a strategy takes before stopping.

Timeline of the question this mission formalizes:

• Chernoff (1959) introduced sequential tests based on generalized likelihood ratios for adaptive design of experiments, with a finite set of hypotheses (doi:10.1214/aoms/1177706205).
• Kaufmann, Cappé and Garivier (2016, JMLR) proved a change-of-measure lower bound on the sample complexity of every $\delta$-PAC strategy (arXiv:1407.4443).
• Garivier and Kaufmann (COLT 2016) identified the exact constant $T^*(\boldsymbol\mu)$ in that lower bound and gave the first strategy, Track-and-Stop, whose sample complexity matches it asymptotically as $\delta\to0$ (arXiv:1602.04589). This mission covers the upper-bound half of that paper.

Setting

A canonical one-parameter exponential family is a family of laws $\nu_\theta$, $\theta\in\Theta$, on $\mathbb R$ with density $\exp(\theta x-b(\theta))$ with respect to a reference measure $\xi$; $b$ is twice differentiable and strictly convex, and $\nu_\theta$ has mean $\dot b(\theta)$. Bernoulli, Poisson and Gaussian laws with known variance are examples. The divergence $d(\mu,\mu')$ is the Kullback–Leibler divergence between the members with means $\mu$ and $\mu'$.

A bandit model $\boldsymbol\mu=(\mu_1,\dots,\mu_K)$ assigns a member of the family to each arm. The class $\mathcal S$ consists of models with a unique optimal arm $a^*(\boldsymbol\mu)$. At each round $t=1,2,\dots$ the learner picks an arm $A_t$ as a function of past observations, observes a reward drawn from that arm's law, and at a stopping time $\tau_\delta$ recommends an arm. $N_a(t)$ is the number of draws of arm $a$ in the first $t$ rounds and $\hat\mu_a(t)$ its empirical mean.

With $\mathrm{Alt}(\boldsymbol\mu)=\{\boldsymbol\lambda\in\mathcal S: a^*(\boldsymbol\lambda)\ne a^*(\boldsymbol\mu)\}$ and $\Sigma_K$ the probability simplex, the characteristic time is

and the maximizer $w^*(\boldsymbol\mu)$ are the optimal proportions of arm draws.

Track-and-Stop combines two ingredients:

• a sampling rule that tracks the plug-in proportions $w^*(\hat{\boldsymbol\mu}(t))$ while forcing each arm to be drawn about $\sqrt t$ times: C-Tracking tracks the cumulated sum of projections of $w^*(\hat{\boldsymbol\mu}(s))$ onto $\Sigma^{\epsilon_s}_K=\{w\in\Sigma_K: w_a\ge\epsilon_s\}$, $\epsilon_s=(K^2+s)^{-1/2}/2$; D-Tracking draws an under-sampled arm when some $N_a(t)<\sqrt t-K/2$, and otherwise the arm maximizing $t\,w^*_a(\hat{\boldsymbol\mu}(t))-N_a(t)$;
• Chernoff's stopping rule, which stops at the first $t$ at which some arm $a$ beats every other arm $b$ in a generalized likelihood ratio test, $Z_{a,b}(t)>\beta(t,\delta)$, here with $\beta(t,\delta)=\log(r(t)/\delta)$.

Formalization targets

Goal: Theorem 14 (p. 13)

For $\alpha\in[1,e/2]$ and $r(t)=O(t^\alpha)$, Chernoff's stopping rule with $\beta(t,\delta)=\log(r(t)/\delta)$ combined with C-Tracking or D-Tracking satisfies

for every $\boldsymbol\mu\in\mathcal S$.

Milestones

• Lemma 15 (p. 20): greedy tracking of cumulated proportions $P(k)$ keeps $\max_i|N_i(n)-P_i(n)|\le K-1$.
• Lemma 7 (p. 7): C-Tracking ensures $N_a(t)\ge\sqrt{t+K^2}-2K$ and $\max_a|N_a(t)-\sum_{s<t}w^*_a(\hat{\boldsymbol\mu}(s))|\le K(1+\sqrt t)$.
• Lemma 8 (p. 7): D-Tracking ensures $N_a(t)\ge(\sqrt t-K/2)_+-1$, and proportions within $3(K-1)\epsilon$ of $w^*(\boldsymbol\mu)$ after a time $t_\epsilon$ that does not depend on the trajectory, once the plug-in targets are within $\epsilon$.
• Proposition 9 (p. 8): under either rule, $N_a(t)/t\to w^*_a(\boldsymbol\mu)$ almost surely.
• Lemma 18 (p. 27): an explicit $x$ with $c_1x\ge\log(c_2x^\alpha)$ for $\alpha\in[1,e/2]$.
• Proposition 13 (p. 11): with any sampling rule whose proportions converge almost surely to $w^*$, $\tau_\delta<\infty$ almost surely and $\limsup_{\delta\to0}\tau_\delta/\log(1/\delta)\le\alpha T^*(\boldsymbol\mu)$ almost surely.

Significance

Theorem 1 of the same paper shows $\mathbb E_{\boldsymbol\mu}[\tau_\delta]\ge T^*(\boldsymbol\mu)\,\mathrm{kl}(\delta,1-\delta)$ for every $\delta$-PAC strategy, and $\mathrm{kl}(\delta,1-\delta)\sim\log(1/\delta)$. Theorem 14 with $\alpha=1$ therefore shows that the lower bound is attained: $T^*(\boldsymbol\mu)$ is the exact asymptotic sample complexity of best arm identification in exponential family models, and Track-and-Stop is asymptotically optimal.

The result is proved in the paper. What is not available is a machine-checked proof for exponential families. The platform already holds a Lean development of the Gaussian case following Lattimore and Szepesvári, Bandit Algorithms, Ch. 33, stated for one existentially chosen policy with a different threshold. This mission asks for the universal statement: every run of either tracking rule, for every exponential family, with the paper's thresholds. The tracking lemmas (Lemmas 15, 7, 8) are deterministic combinatorics and reusable by any tracking-based algorithm.

Difficulty

The obvious argument plugs the almost-sure behaviour of Proposition 13 into an expectation. That step fails: almost-sure convergence of $\tau_\delta/\log(1/\delta)$ does not control $\mathbb E[\tau_\delta]$, because on the rare events where the empirical means are far from $\boldsymbol\mu$ the stopping time may be very large. Theorem 14 needs a quantitative concentration of $\hat{\boldsymbol\mu}(t)$ on events whose complements have summable probability, which in turn relies on the forced exploration guaranteed by the $\sqrt t$ lower bounds on $N_a(t)$ (the concentration step of App. D, Lemmas 19–20).

A second obstacle is the regularity of $w^*$: the tracking lemmas only transfer convergence of $\hat{\boldsymbol\mu}(t)$ to convergence of $N_a(t)/t$ through the continuity of $\boldsymbol\mu\mapsto w^*(\boldsymbol\mu)$ on $\mathcal S$, proved from the characterization of $w^*$ in §2.2 (Proposition 6). The GLR statistic also needs its closed form (7) near $\boldsymbol\mu$, which requires the empirical means to lie in the interior of the mean space.

Formalization scope

• Model. The exponential family is a structure $(\xi,\Theta,b)$ with $\Theta$ a nonempty open interval, each $\nu_\theta$ normalized, $b$ twice continuously differentiable and $\ddot b>0$ on $\Theta$. Openness and $\ddot b>0$ are added to the paper's "convex, twice differentiable"; strict convexity is what makes $\nu^\mu$ unique. Bandit models are parameter vectors $\theta\in\Theta^K$ with $K\ge2$; arms are indexed $0,\dots,K-1$. $\mathcal S$ is the set of parameter vectors with a unique arm of largest mean $\dot b(\theta_a)$.
• Protocol. Policies, the trajectory law $\mathbb P_{\boldsymbol\mu}$, pull counts, empirical means and $T^*(\boldsymbol\mu)$ are the platform's published definitions (BanditPolicy, BanditTrajectory, TrackAndStop). $T^*$ uses Kullback–Leibler divergences of the arm laws over the class $\mathcal S$ and takes values in $[0,\infty]$. Trajectory coordinate $t$ is round $t+1$. An arm never drawn has empirical mean $0$.
• Target map. $w^*(\hat{\boldsymbol\mu}(t))$ is undefined in the paper when $\hat{\boldsymbol\mu}(t)\notin\mathcal S$ (an unsampled arm, ties, a mean outside $\dot b(\Theta)$). Every tracking statement quantifies over every target map with values in $\Sigma_K$ that returns optimal proportions on $\mathcal S$, over every choice of $L^\infty$ projections, and over every tie-breaking, including randomized ones.
• Stopping rule. The two maxima in $Z_{a,b}(t)$ are suprema over $\Theta$ in the extended reals. $Z_{a,b}(t)>\beta$ is written without subtracting infinities. The stopping time is the first $t\ge1$ at which the test succeeds, $+\infty$ if none. "$r(t)=O(t^\alpha)$" is $r(t)\le Dt^\alpha$ for $t\ge1$; $r>0$ is added so that $\log(r(t)/\delta)$ is defined.
• Values in $[0,\infty]$. Expectations of $\tau_\delta$, the ratios and $T^*$ live in $[0,\infty]$. No statement converts them to reals, so an infinite expected stopping time is never read as $0$.
• Corrections, disclosed. Proposition 9's printed $\mathbb P_w$ is $\mathbb P_{\boldsymbol\mu}$. Lemma 18 adds $c_2/c_1^\alpha>1$ and $x>0$, without which its expressions are undefined.
• Ruled out. Specializing to Gaussian arms, or asserting that some sampling policy achieves the bound, would restate existing platform results and is not this theorem: the goal is about every C-Tracking or D-Tracking run in every exponential family.
• Welcome contributions. Exponential-family facts ($\dot b$ is the mean, the KL formula, concentration of empirical means); the continuity of $w^*$ (Proposition 6, App. A.3); Lemma 17 (App. B.2), from which Lemma 8 follows; the closed form (7) of the GLR statistic.

Selected references

• A. Garivier, E. Kaufmann, Optimal Best Arm Identification with Fixed Confidence, COLT 2016, JMLR W&CP 49. arXiv:1602.04589v2
• E. Kaufmann, O. Cappé, A. Garivier, On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models, JMLR 17, 2016. arXiv:1407.4443
• H. Chernoff, Sequential Design of Experiments, Ann. Math. Statist. 30(3), 1959. doi:10.1214/aoms/1177706205
• T. Lattimore, C. Szepesvári, Bandit Algorithms, Cambridge University Press, 2020, Ch. 33. doi:10.1017/9781108571401

Motivation

Best arm identification with fixed confidence is the pure-exploration counterpart of the multi-armed bandit problem. A learner faces $K$ unknown reward distributions ("arms"), samples them sequentially, and must stop and name the arm with the largest mean, being wrong with probability at most a prescribed $\delta$. The question is how many samples this requires. It arises in adaptive A/B testing, in the selection of the best of several simulated systems (ranking and selection in simulation optimization), and in clinical trials that must declare the best treatment with a guaranteed error rate.

Lower bounds for this problem were first stated in terms of the gaps between means (Mannor and Tsitsiklis, 2004). Kaufmann, Cappé and Garivier (2016) replaced ad hoc changes of measure by a single "transportation" lemma relating expected sample counts, Kullback–Leibler divergences and the error probability. Garivier and Kaufmann (COLT 2016, arXiv:1602.04589v2) combine this lemma over all alternative models at once, in the spirit of Graves and Lai (1997), and obtain a lower bound whose constant $T^*(\boldsymbol\mu)$ is exactly matched, as $\delta \to 0$, by their Track-and-Stop strategy. This mission formalizes that lower bound (Theorem 1 of the paper, p. 3).

Setting

A canonical one-parameter exponential family is given by a reference measure $\xi$ on $\mathbb R$, an open interval $\Theta \subset \mathbb R$ and a function $b$, twice continuously differentiable on $\Theta$ with $\ddot b > 0$, such that the laws $\nu_\theta$ with density $\exp(\theta x - b(\theta))$ with respect to $\xi$ are probability measures for $\theta \in \Theta$. The mean of $\nu_\theta$ is $\dot b(\theta)$. Bernoulli laws and Gaussian laws of known variance are examples.

A bandit model is a vector $\theta = (\theta_1, \dots, \theta_K) \in \Theta^K$; arm $a$ returns i.i.d. rewards with law $\nu_{\theta_a}$ and mean $\mu_a = \dot b(\theta_a)$. Arm $a^*(\boldsymbol\mu)$ is the unique optimal arm if $\mu_{a^*} > \mu_a$ for every $a \ne a^*$. Let $\mathcal S$ be any set of bandit models of the family each having a unique optimal arm, and put $\mathrm{Alt}(\boldsymbol\mu) = \{\boldsymbol\lambda \in \mathcal S : a^*(\boldsymbol\lambda) \ne a^*(\boldsymbol\mu)\}$.

A strategy consists of a sampling rule $\pi$ (the arm $A_t$ drawn at round $t$ depends, possibly with extra randomization, on the first $t - 1$ observations), a stopping time $\tau$ of the natural filtration $\mathcal F_t = \sigma(A_1, X_1, \dots, A_t, X_t)$, and an $\mathcal F_\tau$-measurable decision $\hat a_\tau$. It is $\delta$-PAC on $\mathcal S$ if for every $\boldsymbol\mu \in \mathcal S$, $\mathbb P_{\boldsymbol\mu}(\tau < \infty) = 1$ and $\mathbb P_{\boldsymbol\mu}(\hat a_\tau \ne a^*(\boldsymbol\mu)) \le \delta$. $N_a(t)$ is the number of draws of arm $a$ among the first $t$ rounds.

Write $d(\mu_a, \lambda_a) = \mathrm{KL}(\nu_{\theta_a}, \nu_{\lambda_a})$ for the divergence between two arm laws, $\mathrm{kl}(x, y) = x\log\frac{x}{y} + (1 - x)\log\frac{1 - x}{1 - y}$, and $\Sigma_K$ for the probability simplex on the $K$ arms. The characteristic time is defined by eq. (1):

Formalization targets

Goal: Theorem 1 (p. 3)

For $\delta \in (0, 1/2]$, every $\delta$-PAC strategy on $\mathcal S$ and every $\boldsymbol\mu \in \mathcal S$,

The statement fixes no constant beyond those of the paper, and it holds for every $\delta$, not only in the limit.

Milestone: eq. (2) (p. 4)

For every $\boldsymbol\lambda \in \mathcal S$ with $a^*(\boldsymbol\lambda) \ne a^*(\boldsymbol\mu)$,

This is Lemma 1 of Kaufmann et al. (2016), which the paper quotes without proof; Theorem 1 follows from it for every alternative simultaneously.

Significance

Theorem 1 identifies $T^*(\boldsymbol\mu)$ as the exact problem-dependent complexity of fixed-confidence best arm identification: since $\mathrm{kl}(\delta, 1 - \delta) \sim \log(1/\delta)$, it gives $\liminf_{\delta \to 0} \mathbb E_{\boldsymbol\mu}[\tau_\delta]/\log(1/\delta) \ge T^*(\boldsymbol\mu)$, and the paper's Track-and-Stop strategy attains this rate (the subject of mission IV of this series). The bound also explains which proportions of draws an optimal strategy must use: the maximizer $w^*(\boldsymbol\mu)$ of eq. (1) (mission II).

Both results are proved in the literature. Neither is formalized in the paper's generality. The platform holds the textbook form of Lattimore and Szepesvári (Theorem 33.5), which is stated for an arbitrary class with the weaker constant $\log(1/(4\delta))$; for $\delta \le 1/2$, $\mathrm{kl}(\delta, 1 - \delta) \ge \log(1/(2.4\delta)) > \log(1/(4\delta))$, so Theorem 1 is strictly stronger. A formal proof here yields the transportation lemma for exponential families on the platform's infinite-horizon bandit model, which later missions (II–IV, and any lower bound by change of measure) can reuse.

Difficulty

The obvious proof applies the finite-horizon divergence decomposition $\mathrm{KL}(\mathbb P^n_{\boldsymbol\mu}, \mathbb P^n_{\boldsymbol\lambda}) = \sum_a \mathbb E_{\boldsymbol\mu}[N_a(n)]\,d(\mu_a, \lambda_a)$ at a deterministic horizon $n$. That fails here: $\tau$ is random and unbounded, the decision is $\mathcal F_\tau$-measurable, and the relevant divergence is between the laws of the stopped observations. The step from a fixed horizon to a stopping time, together with the data-processing inequality that turns the error guarantees under two models into $\mathrm{kl}(\delta, 1 - \delta)$, is the central difficulty. A second, smaller difficulty is to identify the paper's divergence $d$ and its means $\dot b(\theta)$ with the measure-theoretic KL divergence and mean of the arm laws of the exponential family.

Formalization scope

Lean namespace OptimalBAI.LowerBound. The bandit protocol is the platform's (BanditAlgorithm.BanditPolicy, banditTrajMeasure, IsBanditStoppingTime, IsSoundBAI, baiComplexity); $\mathrm{kl}$ is the platform's bernoulliRelativeEntropy and $N_a(t)$ is trajPullCount. Conventions:

• arms are Fin K, 0-based (the paper's arm $a$ is index $a - 1$); trajectory coordinate $t$ is round $t + 1$;
• $\Theta$ is a nonempty open interval and $\ddot b > 0$ on $\Theta$ (added: the paper says $b$ is convex and twice differentiable; strict convexity is what makes "the unique distribution with mean $\mu$" meaningful); the paper's $d$ is written as the KL divergence of the arm laws (its first equality on p. 3), and the unique optimal arm is defined through the parameter means $\dot b(\theta_a)$;
• $\mathcal S$ is an arbitrary set of models with a unique optimal arm, not the specific set the paper fixes from p. 4 on;
• $\delta$-PAC keeps both halves of the paper's definition (almost-sure stopping and error at most $\delta$);
• $T^*(\boldsymbol\mu)$, divergences and expectations of $\tau$ take values in $[0, \infty]$, never truncated to reals; $T^* = 0$ when $\mathrm{Alt}(\boldsymbol\mu) = \emptyset$ and $T^* = \infty$ when the supremum in eq. (1) is $0$;
• $\delta \le 1/2$ is added. The paper states $\delta \in (0, 1)$, but the theorem and eq. (2) are false for $\delta \in (1/2, 1)$: with two unit-variance Gaussian arms, drawing arm 1 once and naming arm 1 exactly when the fractional part of the reward is below $1/2$ is $0.9$-PAC, while $T^*(\boldsymbol\mu) \to \infty$ as the two means merge. At $\delta = 1/2$ the bound is $0$.

A statement with $\log(1/(4\delta))$ in place of $\mathrm{kl}(\delta, 1 - \delta)$, or restricted to Gaussian arms, is the platform's existing textbook theorem and does not count as this mission's goal; nor does any version that drops the almost-sure stopping clause or truncates $\mathbb E[\tau]$ or $T^*$ to real numbers.

Needed infrastructure: the transportation lemma at a stopping time (data processing for KL through an $\mathcal F_\tau$-measurable event, Wald-type identity for the stopped log-likelihood ratio), the identities "mean of $\nu_\theta$ $= \dot b(\theta)$" and "KL of two family members $= b(\theta') - b(\theta) - \dot b(\theta)(\theta' - \theta)$", and $\mathbb E[\tau] = \sum_a \mathbb E[N_a(\tau)]$. All are reusable beyond this mission. Contributions of any of these lemmas, of eq. (2) alone, or of the Gaussian and Bernoulli special cases as stepping stones are welcome.

Selected references

• A. Garivier, E. Kaufmann, Optimal Best Arm Identification with Fixed Confidence, COLT 2016 (JMLR W&CP 49), arXiv:1602.04589v2. https://arxiv.org/abs/1602.04589
• E. Kaufmann, O. Cappé, A. Garivier, On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models, Journal of Machine Learning Research 17(1), 2016. https://arxiv.org/abs/1407.4443
• T. L. Graves, T. L. Lai, Asymptotically Efficient Adaptive Choice of Control Laws in Controlled Markov Chains, SIAM Journal on Control and Optimization 35(3), 1997. https://doi.org/10.1137/S0363012994275440
• S. Mannor, J. N. Tsitsiklis, The Sample Complexity of Exploration in the Multi-Armed Bandit Problem, Journal of Machine Learning Research 5, 2004. https://www.jmlr.org/papers/v5/mannor04b.html
• T. Lattimore, C. Szepesvári, Bandit Algorithms, Cambridge University Press, 2020, Chapter 33. https://doi.org/10.1017/9781108571401

Motivation

The stochastic multi-armed bandit is the basic model of sequential decision making under uncertainty: a learner repeatedly chooses one of $d$ actions, observes a noisy reward for the chosen action only, and must balance exploring poorly known actions against exploiting the one that currently looks best. It underlies adaptive clinical trials, online advertising, recommendation, dynamic pricing and many simulation-optimization procedures in operations research.

The standard algorithm is UCB (Auer, Cesa-Bianchi and Fischer, 2002, doi:10.1023/A:1013689704352), which plays the arm with the largest upper confidence bound on its mean. Its confidence widths grow with the current time $t$ (or with a horizon $n$ fixed in advance), and its guarantee is on the expected regret, which grows like $\log n$. Lai and Robbins (1985, doi:10.1016/0196-8858(85)90002-8) showed that logarithmic growth of the expected regret cannot be avoided by a consistent algorithm.

Abbasi-Yadkori, Pál and Szepesvári (NIPS 2011) proved a self-normalized concentration inequality for vector-valued martingales that holds uniformly over time. Section 6 of their paper applies it to the $d$-armed bandit. The resulting confidence intervals depend on neither the horizon nor the current time. The UCB variant built on them, UCB($\delta$), has pseudo-regret bounded by a constant, independent of the horizon, on a single event of probability at least $1-\delta$. This mission formalizes that section.

Setting

There are $d \ge 1$ arms with unknown means $\mu_1, \dots, \mu_d \in \mathbb R$. Write $\mu_* = \max_{1 \le i \le d} \mu_i$ for the best mean and $\Delta_i = \mu_* - \mu_i \ge 0$ for the gap of arm $i$.

Randomness lives on a probability space $(\Omega, \mathcal F, P)$ with a filtration $(\mathcal F_t)_{t \ge 0}$. In round $t = 1, 2, \dots$ the learner plays an arm $I_t$ that is $\mathcal F_{t-1}$-measurable and receives the reward $\mu_{I_t} + \eta_t$. The noise $\eta_t$ is $\mathcal F_t$-measurable and conditionally 1-sub-Gaussian:

The noise need not be independent or identically distributed across rounds, and the rewards need not be bounded.

After $t$ rounds, $N_{i,t}$ is the number of plays of arm $i$ and $\overline X_{i,t}$ is the average reward received from it. For a confidence level $\delta > 0$ the confidence width is

with $c_{i,t} = +\infty$ when $N_{i,t} = 0$. UCB($\delta$) plays, in round $t$, an arm that maximizes $\overline X_{i,t-1} + c_{i,t-1}$; in particular every arm is played once before any comparison is made. The pseudo-regret after $n$ rounds is

This is the linear bandit of the paper with the standard basis of $\mathbb R^d$ as decision set and $\theta_* = \mu$.

Formalization targets

Goal: Theorem 7 (constant regret of UCB($\delta$))

For every $\delta > 0$, with probability at least $1-\delta$, for all $n \ge 0$ simultaneously,

The right-hand side depends only on the gaps, $d$ and $\delta$.

Milestone: Lemma 6 (confidence intervals)

For any adapted choice of arms (not only UCB($\delta$)) and every $\delta > 0$, with probability at least $1-\delta$,

Significance

The result. Theorem 7 shows that, once a confidence level is fixed, a UCB-type algorithm can stop paying for exploration after finitely many rounds: with probability $1-\delta$ the total regret over an infinite horizon is bounded. The paper notes that this does not contradict the Lai–Robbins lower bound, which concerns expected regret; on the failure event of probability $\delta$ the regret may grow linearly. Lemma 6 is the practical content behind this: anytime-valid confidence intervals for adaptively sampled means under martingale noise, usable for stopping rules, best-arm identification and any sequential procedure that inspects its estimates at data-dependent times.

Formalizing it. Both results are proved in the paper's appendices (F and G). No machine-checked proof of either is known to exist. The platform already has the self-normalized martingale bound for $V = \lambda I$ and unit sub-Gaussian noise (BanditAlgorithm.self_normalized_martingale_bound, listed here as a reference item), and UCB bounds with horizon-dependent widths and in-expectation or pathwise conclusions. Neither is Theorem 7. The mission produces a formal account of time-uniform confidence intervals for adaptively sampled arm means, and of a regret bound that is uniform in the horizon.

Difficulty

Two points separate this from textbook UCB analyses. First, the number of samples $N_{i,t}$ of an arm is itself random and depends on past noise, so a fixed-sample concentration inequality with a union bound over times does not give widths that are free of $t$: a union bound over all $t$ costs a factor that diverges. The time-uniform event must come from a maximal (self-normalized) inequality applied to the martingale $\sum_s \eta_s \mathbf 1\{I_s = i\}$. Second, the regret statement is uniform in $n$ with constants depending on the gaps; converting a condition of the form "$c(N) \ge \Delta_i/2$" into an explicit bound on $N$ requires solving an inequality in which $N$ appears both polynomially and inside a logarithm, and the explicit constants $3$ and $16$ must come out of that step.

Formalization scope

The Lean development lives in the namespace ImprovedLinBandits.UCBDelta.

• Arms are Fin d with 0 < d in the goal; means are μ : Fin d → ℝ, $\mu_*$ is ⨆ i, μ i (the maximum over a nonempty finite type).
• Rounds are indexed t + 1 for t : ℕ: the arm I (t + 1) is ℱ t-measurable, the noise η (t + 1) is ℱ (t + 1)-measurable and satisfies Mathlib's HasCondSubgaussianMGF (ℱ t) … (η (t + 1)) 1 P. Values at index $0$ are unused.
• The sample space is assumed to be a standard Borel space, which Mathlib's conditional sub-Gaussianity requires; this hypothesis is not in the paper.
• "With probability at least $1-\delta$, for all …" is stated as: the outer probability of the failure event, with the quantifiers over arms and times inside it, is at most $\delta$. For $\delta \ge 1$ the statements are trivially true, as in the paper.
• The rule (4) is read with the statistics of rounds $1, \dots, t-1$, because the printed $\overline X_{i,t}$, $c_{i,t}$ already count round $t$. An unplayed arm, whose width is $+\infty$ in the paper, is played before the indices are compared. Every tie-breaking rule is allowed, and measurability of the chosen arm is assumed rather than derived.
• The printed Theorem 7 has no quantifier on $n$; it is formalized in the uniform-in-$n$ form, matching the section's claim of constant regret and the time-uniform event of Lemma 6.
• Lean's division by zero makes $\overline X_{i,t}$ and $c_{i,t}$ equal to $0$ when $N_{i,t} = 0$. Lemma 6 therefore excludes $N_{i,t} = 0$ explicitly (where the paper's inequality is vacuous), and the run predicate handles unplayed arms separately. The regret bound sums over arms with $\Delta_i > 0$, so its divisions are well defined.

A trivializing formalization is ruled out: the confidence event is not assumed as a hypothesis of Theorem 7, the i.i.d. bandit model is not substituted for the martingale noise model, and no bound on the means or rewards is imposed.

Useful infrastructure for solvers: the self-normalized bound of Theorem 1 in the scalar case ($d = 1$, $\lambda = 1$, $A_s = \mathbf 1\{I_s = i\}$, $\overline V_t = 1 + N_{i,t}$), a union bound over arms, and elementary inequalities inverting $N \mapsto \frac{1+N}{N^2}(1 + 2\log(d\sqrt{1+N}/\delta))$. Lemmas about pull counts and empirical means under adaptive sampling are reusable beyond this mission and are welcome as contributions.

Selected references

• Y. Abbasi-Yadkori, D. Pál, Cs. Szepesvári, Improved Algorithms for Linear Stochastic Bandits, Advances in Neural Information Processing Systems 24 (NIPS), 2011. https://papers.nips.cc/paper/2011/hash/e1d5be1c7f2f456670de3d53c7b54f4a-Abstract.html
• P. Auer, N. Cesa-Bianchi, P. Fischer, Finite-time Analysis of the Multiarmed Bandit Problem, Machine Learning 47, 2002. https://doi.org/10.1023/A:1013689704352
• T. L. Lai, H. Robbins, Asymptotically Efficient Adaptive Allocation Rules, Advances in Applied Mathematics 6, 1985. https://doi.org/10.1016/0196-8858(85)90002-8
• J.-Y. Audibert, R. Munos, Cs. Szepesvári, Exploration–exploitation tradeoff using variance estimates in multi-armed bandits, Theoretical Computer Science 410, 2009. https://doi.org/10.1016/j.tcs.2009.01.016

Motivation

Online convex optimization is a repeated game between a learner and an adversary. In each round $t=1,\dots,T$ the learner commits to a point $x_t$ of a convex set $\mathcal P\subseteq\mathbb R^n$; only then is a convex cost function $f_t$ revealed, and the learner pays $f_t(x_t)$. The learner is judged by its regret: its total cost minus the total cost of the best fixed point chosen in hindsight. The model covers online portfolio selection, online regression, prediction with expert advice and the analysis of stochastic gradient methods, and it is the standard language of online learning theory.

Zinkevich (ICML 2003) showed that projected gradient descent with step sizes of order $1/\sqrt t$ has regret $O(GD\sqrt T)$ for any convex costs with gradients bounded by $G$ on a set of diameter $D$, and this rate cannot be improved for linear costs. Hazan, Agarwal and Kale (Machine Learning 69, 2007) asked what curvature buys. Their first result, the subject of this mission, is that the same algorithm with the faster step sizes $1/(Ht)$ has regret only logarithmic in $T$ once every cost function is $H$-strongly convex. The paper's other three results (the Online Newton Step, Follow the Approximate Leader and Exponentially Weighted Online Optimization, for exp-concave costs) are the subject of the other missions of this series.

Setting

Fix $n\in\mathbb N$ and a nonempty, closed, bounded, convex set $\mathcal P\subseteq\mathbb R^n$, with the Euclidean norm $\|\cdot\|_2$ (§2.1, p. 171).

• Cost functions. A sequence $f_1,f_2,\dots:\mathbb R^n\to\mathbb R$. Write $\nabla f_t(x)$ for the gradient and $\nabla^2 f_t(x)$ for the Hessian.
• Gradient bound. A number $G$ with $\|\nabla f_t(x)\|_2\le G$ for all $x\in\mathcal P$ and all rounds $t$ (p. 172).
• $H$-strong convexity (p. 172). For $H>0$, $f$ is $H$-strongly convex on $\mathcal P$ when it is twice differentiable and $\nabla^2 f(x)\succeq H I_n$ for every $x\in\mathcal P$, i.e. $v^\top\nabla^2 f(x)v\ge H\|v\|_2^2$ for all $v$.
• Euclidean projection. $\Pi_{\mathcal P}(y)$ is the point of $\mathcal P$ nearest to $y$, $\Pi_{\mathcal P}(y)=\arg\min_{x\in\mathcal P}\|x-y\|_2$ (IsProj P y z).
• Online Gradient Descent (Fig. 1, p. 174), with step sizes $\eta_1,\eta_2,\dots$: $x_1\in\mathcal P$ is arbitrary, and in iteration $t>1$

(IsOGDRun P η f x).

• Regret (p. 171): $\mathrm{Regret}_T=\sum_{t=1}^T f_t(x_t)-\min_{x\in\mathcal P}\sum_{t=1}^T f_t(x)$, and $\mathrm{Regret}_T(\mathrm{OGD})$ is its supremum over all cost sequences.

Formalization targets

Goal: Theorem 1 (p. 175)

For $H>0$, cost functions that are $H$-strongly convex on $\mathcal P$ with gradients bounded by $G$ on $\mathcal P$, and any run of Online Gradient Descent whose step after round $t$ is $\eta_{t+1}=1/(Ht)$, for every $T\ge1$ and every $u\in\mathcal P$:

This is LogRegretOCO.OGD.ogd_regret_bound. The constant is the paper's, and at $T=1$ the bound reads $G^2/(2H)$.

Milestones

1. Eq. (1), the strong-convexity inequality: for $x,y\in\mathcal P$, $2(f(x)-f(y))\le 2\nabla f(x)^\top(x-y)-H\|y-x\|_2^2$.
2. Lemma 8 with $A=I_n$: for a convex $\mathcal P$, $z=\Pi_{\mathcal P}(y)$ and $a\in\mathcal P$, $\|y-a\|_2^2\ge\|z-a\|_2^2$. This is already on the platform, proved, as UnderstandingML.projection_lemma, and is reused as a reference item.
3. Eq. (2), the one-step inequality: for $z=\Pi_{\mathcal P}(x-\eta g)$, $\eta>0$, $\|g\|_2\le G$ and $u\in\mathcal P$, $2g^\top(x-u)\le(\|x-u\|_2^2-\|z-u\|_2^2)/\eta+\eta G^2$.

The last step of the argument is the harmonic-sum bound $\sum_{t=1}^T 1/t\le 1+\log T$, which Mathlib already provides (harmonic_le_one_add_log).

Significance

The result. Theorem 1 separates two regimes of online convex optimization: $\Theta(\sqrt T)$ regret for general convex costs and $O(\log T)$ for strongly convex ones, with an algorithm that costs one gradient and one projection per round. Through the online-to-batch conversion, the same step-size schedule gives the $O(\log T/T)$ rate of stochastic gradient descent on strongly convex objectives. The theorem is also the reference point for the paper's weaker exp-concavity assumption, under which the other three algorithms obtain $O(n\log T)$ regret.

Formalizing it. The result is proved and well known; what this mission adds is a machine-checked proof with the paper's exact constant. The Prove2Me platform holds a statement of the textbook version of this theorem (Hazan, Introduction to Online Convex Optimization, Theorem 3.3), OnlineConvexOpt.FirstOrder.online_gradient_descent_strongly_convex_regret, but it is Disproved: its regret is written with a real infimum over the decision set, which Lean evaluates to a junk value. No proved version of the logarithmic bound is on the platform. The strong-convexity inequality, the one-step projected-gradient inequality and the telescoping argument are reusable by every later formalization of gradient methods on the platform.

Difficulty

The argument is short, and its difficulty lies in the bookkeeping. The telescoping sum of squared distances cancels exactly only with the right step-size indexing: the step taken after round $t$ must be $1/(Ht)$. Reading Fig. 1 literally with $\eta_t=1/(Ht)$ makes the step after round $t$ equal to $1/(H(t+1))$, and the sum then leaves an uncancelled term $\frac H2\|x_1-x^*\|_2^2$ that the printed bound does not contain. Strong convexity is a statement about the Hessian, so the curvature inequality (1) needs a second-order Taylor expansion along a segment of $\mathcal P$; convexity of $\mathcal P$ keeps the segment inside the set where the Hessian bound holds. The projection step needs the obtuse-angle property of Euclidean projection onto a convex set.