1 Introduction
This paper considers the following fundamental question: Given an unknown convex function , and the ability to query for (possibly noisy) realizations of its values at various points, how can we optimize with as few queries as possible?
This question, under different guises, has played an important role in several communities. In the optimization community, this is usually known as “zerothorder” or “derivativefree” convex optimization, since we only have access to function values rather than gradients or higherorder information. The goal is to return a point with small optimization error on some convex domain, using a limited number of queries. Derivativefree methods were among the earliest algorithms to numerically solve unconstrained optimization problems, and have recently enjoyed increasing interest, being especial useful in blackbox situations where gradient information is hard to compute or does not exist [Nesterov(2011), Stich et al.(2011)Stich, Müller, and Gärtner]. In a stochastic framework, we can only obtain noisy realizations of the function values (for instance, due to running the optimization process on sampled data). We refer to this setting as derivativefree SCO (short for stochastic convex optimization).
In the learning community, these kinds of problems have been closely studied in the context of multiarmed bandits and (more generally) bandit online optimization, which are powerful models for sequential decision making under uncertainty [CesaBianchi and Lugosi(2006), Bubeck and CesaBianchi(2012)]. In a stochastic framework, these settings correspond to repeatedly choosing points in some convex domain, obtaining noisy realizations of some underlying convex function’s value. However, rather than minimizing optimization error, our goal is to minimize the (average) regret: roughly speaking, that the average of the function values we obtain is not much larger than the minimal function value. For example, the wellknown multiarmed bandit problem corresponds to a linear function over the simplex. We refer to this setting as bandit SCO. As will be more explicitly discussed later on, any algorithm which attains small average regret can be converted to an algorithm with the same optimization error. In other words, bandit SCO is only harder than derivativefree SCO. We note that in the context of stochastic multiarmed bandits, the potential gap between the two settings (under the terms “cumulative regret” and “simple regret”) was introduced and studied in [Bubeck et al.(2011)Bubeck, Munos, and Stoltz].
When one is given gradient information, the attainable optimization error / average regret is wellknown: under mild conditions, it is for convex functions and for stronglyconvex functions, where is the number of queries [Zinkevich(2003), Hazan and Kale(2011), Rakhlin et al.(2012)Rakhlin, Shamir, and Sridharan]. Note that these bounds do not explicitly depend on the dimension of the domain.
The inherent complexity of bandit/derivativefree SCO is not as wellunderstood. An important exception is multiarmed bandits, where the attainable error/regret is known to be exactly , where is the dimension and is the number of queries^{1}^{1}1 In a stochastic setting, a more common bound in the literature is , but the notation hides a nontrivial dependence on the form of the underlying linear function (in multiarmed bandits terminology, a gap between the expected rewards bounded away from ). Such assumptions are not natural in a nonlinear bandits SCO setup, and without them, the regret is indeed . See for instance [Bubeck and CesaBianchi(2012), Chapter 2] for more details. [Auer et al.(2002)Auer, CesaBianchi, Freund, and Schapire, Audibert and Bubeck(2009)]. Linear functions over other convex domains has also been explored, with upper bounds on the order of to (e.g. [AbbasiYadkori et al.(2011)AbbasiYadkori, Pál, and Szepesvári, Bubeck et al.(2012)Bubeck, CesaBianchi, and Kakade]). For linear functions over general domains, informationtheoretic lower bounds have been proven in [Dani et al.(2007)Dani, Hayes, and Kakade, Dani et al.(2008)Dani, Hayes, and Kakade, Audibert et al.(2011)Audibert, Bubeck, and Lugosi]. However, these lower bounds are either on the regret (not optimization error); shown for nonconvex domains; or are implicit and rely on artificial, carefully constructed domains. In contrast, we focus here on simple, natural domains and convex problems.
When dealing with more general, nonlinear functions, much less is known. The problem was originally considered over 30 years ago, in the seminal work by Yudin and Nemirovsky on the complexity of optimization [Nemirovsky and Yudin(1983)]. The authors provided some algorithms and upper bounds, but as they themselves emphasize (cf. pg. 359), the attainable complexity is far from clear. Quite recently, [Jamieson et al.(2012)Jamieson, Nowak, and Recht] provided an lower bound for stronglyconvex functions, which demonstrates that the “fast” rate in terms of , that one enjoys with gradient information, is not possible here. In contrast, the current bestknown upper bounds are for convex, stronglyconvex, and stronglyconvexandsmooth functions respectively ([Flaxman et al.(2005)Flaxman, Kalai, and McMahan, Agarwal et al.(2010)Agarwal, Dekel, and Xiao]); And a bound for convex functions ([Agarwal et al.(2011)Agarwal, Foster, Hsu, Kakade, and Rakhlin]), which is better in terms of dependence on but very bad in terms of the dimension .
In this paper, we investigate the complexity of bandit and derivativefree stochastic convex optimization, focusing on nonlinear functions, with the following contributions (see also the summary in Table 1):

We prove that for stronglyconvex and smooth functions, the attainable error/regret is exactly . This has three important ramifications: First of all, it settles the question of attainable performance for such functions, and is the first sharp characterization of complexity for a general nonlinear bandit/derivativefree class of problems. Second, it proves that the required number of queries in such problems must scale quadratically with the dimension, even in the easier optimization setting, and in contrast to the linear case which often allows linear scaling with the dimension. Third, it formally provides a natural lower bound for more general classes of convex problems.

We analyze an important special case of stronglyconvex and smooth functions, namely quadratic functions. We show that for such functions, one can (efficiently) attain optimization error, and that this rate is sharp. To the best of our knowledge, it is the first general class of nonlinear functions for which one can show a “fast rate” (in terms of ) in a derivativefree stochastic setting. In fact, this may seem to contradict the result in [Jamieson et al.(2012)Jamieson, Nowak, and Recht], which shows an lower bound on quadratic functions. However, as we explain in more detail later on, there is no contradiction, since the example establishing the lower bound of [Jamieson et al.(2012)Jamieson, Nowak, and Recht] imposes an extremely small domain (which actually decays with ), while our result holds for a fixed domain. Although this result is tight, we also show that under more restrictive assumptions on the noise process, it is sometimes possible to obtain better error bounds, as good as .

We prove that even for quadratic functions, the attainable average regret is exactly , in contrast to the result for optimization error. This shows there is a real gap between what can be obtained for derivativefree SCO and bandit SCO, without any specific distributional assumptions. Again, this stands in contrast to settings such as multiarmed bandits, where there is no difference in their distributionfree performance.
We emphasize that our upper bounds are based on the assumption that the function minimizer is bounded away from the domain boundary, or that we can query points slightly outside the domain. However, we argue that this assumption is not very restrictive in the context of stronglyconvex functions (especially in learning applications), where the domain is often , and a minimizer always exists.
The paper is structured as follows: In Sec. 2, we formally define the setup and introduce the notation we shall use in the remainder of the paper. For clarity of exposition, we begin with the case of quadratic functions in Sec. 3, providing algorithms, upper and lower bounds. The tools and insights we develop for the quadratic case will allow us to tackle the more general stronglyconvexandsmooth setting in Sec. 4. We end the main part of the paper with a summary and discussion of open problems in Sec. 5. In Appendix A, we demonstrate that one can obtain improved performance in the quadratic case, if we’re considering more specific natural noise processes. Additional proofs are presented in Appendix B.
Optimization Error  Average Regret  

Function Type  
Quadratic  
Str. Convex and Smooth  
Str. Convex  
Convex 
2 Preliminaries
Let denote the standard Euclidean norm. We let denote the convex function of interest, where is a (closed) convex domain. We say that is strongly convex, for , if for any and any subgradient of at , it holds that . Intuitively, this means that we can lower bound everywhere by a quadratic function of fixed curvature. We say that is smooth if for any , and any subgradient of at , it holds that . Intuitively, this means that we can upperbound everywhere by a quadratic function of fixed curvature. We let denote a minimizer of on . To prevent trivialities, we consider in this paper only functions whose optimum is known beforehand to lie in some bounded domain (even if is large or all of ), and the function is Lipschitz in that domain.
The learning/optimization process proceeds in rounds. Each round , we pick and query a point , obtaining an independent realization of , where
is an unknown zeromean random variable, such
^{2}^{2}2We note that this slightly deviates from the more common assumption in the bandits/derivativefree SCO setting that . While such assumptions are equivalent for bounded , we also wish to consider cases with unrestricted domains . In that case, assuming may lead to trivialities in the derivativefree setting. For example, consider the case where . Then for any and anywith uniformly bounded variance, we can get a virtually noiseless estimate of
by picking for some large and computing . Variants of this idea will also allow virtually noiseless estimates of the linear term. that . In the bandit SCO setting, our goal is to minimize the expected average regret, namelywhereas in the derivativefree SCO setting, our goal is to compute, based on and the observed values, some point , such that the expected optimization error
is as small as possible. We note that given a bandit SCO algorithm with some regret bound, one can get a derivativefree SCO algorithm with the same optimization error bound: we simply run the stochastic bandit algorithm, getting , and returning . By Jensen’s inequality, the expected optimization error is at most the expected average regret with respect to . Thus, bandit SCO is only harder than derivativefree SCO.
In this paper, we provide upper and lower bounds on the attainable optimization error / average regret, as a function of the dimension and the number of rounds/queries
. For simplicity, we focus here on bounds which hold in expectation, and an interesting point for further research is to extend these to bounds on the actual error/regret, which hold with high probability.
3 Quadratic Functions
In this section, we consider the class of quadratic functions, which have the form
where
is positivedefinite (with a minimal eigenvalue bounded away from
). Moreover, to make the problem wellbehaved, we assume that has a spectral norm of at most , and that . We note that if the norms are bounded but larger than, this can be easily handled by rescaling the function. It is easily seen that such functions are both strongly convex and smooth. Moreover, this is a natural and important class of functions, which in learning applications appears, for instance, in the context of least squares and ridge regression. Besides providing new insights for this class, we will use the techniques developed here later on, in the more general case of stronglyconvex and smooth functions.
3.1 Upper Bounds
We begin by showing that for derivativefree SCO, one can obtain an optimization error bound of . To the best of our knowledge, this is the first example of a derivativefree stochastic bound scaling as for a general class of nonlinear functions, as opposed to . However, to achieve this result, we need to make the following mild assumption:
Assumption 1
At least one of the following holds for some fixed :

The quadratic function attains its minimum in the domain , and the Euclidean distance of from the domain boundary is at least .

We can query not just points in , but any point whose distance from is at most .
With stronglyconvex functions, the most common case is that , and then both cases actually hold for any value of . Even in other situations, one of these assumptions virtually always holds. Note that we crucially rely here on the strongconvexity assumption: with (say) linear functions, the domain must always be bounded and the optimum always lies at the boundary of the domain.
With this assumption, the bound we obtain is on the order of . As discussed earlier, [Jamieson et al.(2012)Jamieson, Nowak, and Recht] recently proved a lower bound for derivativefree SCO, which actually applies to quadratic functions. This does not contradict our result, since in their example the diameter of (and hence also ) decays with . In contrast, our bound holds for fixed , which we believe is natural in most applications.
To obtain this behavior, we utilize a wellknown
point gradient estimate technique, which allows us to get an unbiased estimate of the gradient at any point by randomly querying for a (noisy) value of the function around it (see
[Nemirovsky and Yudin(1983), Flaxman et al.(2005)Flaxman, Kalai, and McMahan]). Our key insight is that whereas for general functions one must query very close to the point of interest (scaling to with ), quadratic functions have additional structure which allows us to query relatively far away, allowing gradient estimates with much smaller variance.The algorithm we use is presented as Algorithm 1, and is computationally efficient. It uses a modification of the domain , defined as follows. First, we let denote some known upper bound on . If the first alternative of assumption 1 holds, then consists of all points in , whose distance from ’s boundary is at least . If the second alternative holds, then . Note that under any alternative, it holds that is convex, that , that , and that our algorithm always queries at legitimate points. In the pseudocode, we use to denote projection on . For simplicity, we assume that is an integer and that includes the origin .
The following theorem quantifies the optimization error of our algorithm. Let be a strongly convex function, where are all at most , and suppose the optimum has a norm of at most . Then under Assumption 1, the point returned by Algorithm 1 satisfies
Note that returning as the average over the last iterates (as opposed to averaging over all iterates) is necessary to avoid factors [Rakhlin et al.(2012)Rakhlin, Shamir, and Sridharan].
As an interesting sidenote, we conjecture that a gradientbased approach is crucial here to obtain rates (in terms of ). For example, a different family of derivativefree methods (see for instance [Nemirovsky and Yudin(1983), Agarwal et al.(2011)Agarwal, Foster, Hsu, Kakade, and Rakhlin, Jamieson et al.(2012)Jamieson, Nowak, and Recht]) is based on a type of noisy binary search, where a few strategically selected points are repeatedly sampled in order to estimate which of them has a larger/smaller function value. This is used to shrink the feasible region where the optimum might lie. Since it is generally impossible to estimate the mean of noisy function values at a rate better than , it is not clear if one can get an optimization rate faster than with such methods.
The proof of the theorem relies on the following key lemma, whose proof appears in the appendix. For any , we have that
and
This lemma implies that Algorithm 1
essentially performs stochastic gradient descent over the stronglyconvex function
, where the gradient estimates are unbiased and with bounded second moments. The returned point is a suffixaverage of the last
iterates. Using a convergence analysis for stochastic gradient descent with suffixaveraging [Rakhlin et al.(2012)Rakhlin, Shamir, and Sridharan, Theorem 5], and plugging in the bounds of Lemma 3.1, we get Thm. 3.1.3.2 Lower Bounds
In this subsection, we prove that the upper bound obtained in Thm. 3.1 is essentially tight: namely, up to constants, the worstcase error rate one can obtain for derivativefree SCO of quadratic functions is order of . Besides showing that the algorithm above is essentially optimal, it implies that even for extremely nice stronglyconvex functions and domains, the number of queries required to reach some fixed accuracy scales quadratically with the dimension . This stands in contrast to the case of linear functions, where the provable query complexity often scales linearly with .
Let the number of rounds be fixed. Then for any (possibly randomized) querying strategy, there exists a quadratic function of the form , which is minimized at where , such that the resulting satisfies
Note that since , we know in advance that the optimum must lie in the unit Euclidean ball. Despite this, the lower bound holds even if we do not restrict at all the domain in which we are allowed to query  i.e., it can even be all of .
The proof technique is inspired by a lower bound which appears in [AriasCastro et al.(2011)AriasCastro, Candès, and Davenport], in the different context of compressed sensing. The argument also bears some close similarities to the proof of Assouad’s lemma (see [Cybakov(2009)]).
We will exhibit a distribution over quadratic functions , such that in expectation over this distribution, any querying strategy will attain optimization error. This implies that for any querying strategy, there exists some deterministic for which it will have this amount of error.
The functions we shall consider are
where is drawn uniformly from , with being a parameter to be specified later. Moreover, we will assume that the noise
is a Gaussian random variable with zero mean and standard deviation
.By definition of strong convexity, it is easy to verify that . Thus, the expected optimization error (over the querying strategy) is at least
(1) 
We will assume that the querying strategy is deterministic: is a deterministic function of the previous query values at . This assumption is without loss of generality, since any random querying strategy can be seen as a randomization over deterministic querying strategy. Thus, a lower bound which holds uniformly for any deterministic querying strategy would also hold over a randomization.
To lower bound Eq. (1), we use the following key lemma, which relates this to the question of how informative are the query values (as measured by KullbackLeibler or KL divergence) for determining the sign of ’s coordinates. Intuitively, the more similar the query values are, the smaller is the KL divergence and the harder it is to distinguish the true sign of each , leading to a larger lower bound. The proof appears in the appendix.
Let
be a random vector, none of whose coordinates is supported on
, and let be a sequence of query values obtained by a deterministic strategy returning a point (so that the query location is a deterministic function of , and is a deterministic function of ). Then we havewhere
and represents the KL divergence between two distributions.
Using Lemma 3.2, we can get a lower bound for the above, provided an upper bound on the ’s. To analyze this, consider any fixed values of , and any fixed values of . Since the querying strategy is assumed to be deterministic, it follows that is uniquely determined. Given this , the function value equals
(2) 
conditioned on , and
(3) 
conditioned on . Comparing Eq. (2) and Eq. (3
), we notice that they both represent a Gaussian distribution (due to the
noise term), with standard deviation and means seperated by . To bound the divergence, we use the following standard result on the KL divergence between two Gaussians [Kullback(1959)]:Let represent a Gaussian distribution variable with mean and variance . Then
Using this lemma, it follows that
Plugging this upper bound on the ’s in Lemma 3.2, we can further lower bound on the expected optimization error from Eq. (1) by
(4) 
Finally, we choose , and obtain a lower bound of
as required.
The theorem above applies to the optimization error for derivativefree SCO. We now turn to deal with the case of bandit SCO and regret, showing an lower bound. Since the derivativefree SCO bound was , the result implies a real gap between what can be obtained in terms of average regret, as opposed to optimization error, without any specific distributional assumptions. This stands in contrast to settings such as multiarmed bandits, where the construction implying the known lower bound (e.g. [CesaBianchi and Lugosi(2006)]) applies equally well to derivativefree and bandit SCO (see [Bubeck et al.(2011)Bubeck, Munos, and Stoltz]).
Let the number of rounds be fixed. Then for any (possibly randomized) querying strategy, there exists a quadratic function of the form , which is minimized at where , such that
Note that our lower bound holds even when the domain is unrestricted (the algorithm can pick any point in ). Moreover, the lower bound coincides (up to a constant) with the regret upperbound shown for stronglyconvex and smooth functions in [Agarwal et al.(2010)Agarwal, Dekel, and Xiao]. This shows that for stronglyconvex and smooth functions, the minimax average regret is . Also, the lower bound implies that one cannot hope to obtain average regret better than for more general bandit problems, such as stronglyconvex or even convex problems.
The proof relies on techniques similar to the lower bound of Thm. 3.2, with a key additional insight. Specifically, in Thm. 3.2, the lower bound obtained actually depends on the norm of the points (see Eq. (4)), and the optimal has a very small norm. In a regret minimization setting the points cannot be too far from , and thus must have a small norm as well, leading to a stronger lower bound than that of Thm. 3.2. The formal proof appears in the appendix.
4 Strongly Convex and Smooth Functions
We now turn to the more general case of strongly convex and smooth functions. First, we note that in the case of functions which are both strongly convex and smooth, [Agarwal et al.(2010)Agarwal, Dekel, and Xiao, Theorem 14] already provided an average regret bound (which holds even in a nonstochastic setting). The main result of this section is a matching lower bound, which holds even if we look at the much easier case of derivativefree SCO. This lower bound implies that the attainable error for stronglyconvex and smooth functions is order of , and at least for any harder setting.
Let the number of rounds be fixed. Then for any (possibly randomized) querying strategy, there exists a function over which is strongly convex and smooth; Is Lipschitz over the unit Euclidean ball; has a global minimum in the unit ball; And such that the resulting satisfies
Note that we made no attempt to optimize the constant.
The general proof technique is rather similar to that of Thm. 3.2, but the construction is a bit more intricate. Specifically, letting be a parameter to be determined later, we look at functions of the form
where
is uniformly distributed on
. To see the intuition behind this choice, let us consider the onedimensional case (). Recall that in the quadratic setting, the function we considered (in one dimension) was of the formwhere was chosen uniformly at random from , and is a “small” number. Thus, the optimum is at either or , and the difference at these optima is order of . However, by picking , the difference is on the order of  much larger than the difference close to the optimum, which is order of . Therefore, by querying for far from the optimum, and getting noisy values of , it is easier to distinguish whether we are dealing with or , leading to a optimization error bound. In contrast, the function we consider here (in the onedimensional case) is of the form
(5) 
This form is carefully designed so that is order of , not just at the optima of and , but for all . This is because of the additional denominator, which makes the function closer and closer to the larger is  see Fig. 1 for a graphical illustration. As a result, no matter how the function is queried, distinguishing the choice of is difficult, leading to the strong lower bound of Thm. 4. A formal proof is presented in the appendix.
5 Discussion
In this paper, we considered the dual settings of bandit and derivativefree stochastic convex optimization. We provided a sharp characterization of the attainable performance for stronglyconvex and smooth functions. The results also provide useful lowerbounds for more general settings. We also considered the case of quadratic functions, showing that a “fast” rate is possible in a stochastic setting, even without knowledge of derivatives. Our results have several qualitative differences compared to previously known results which focus on linear functions, such as quadratic dependence on the dimension even for extremely “nice” functions, and a provable gap between the attainable performance in bandit optimization and derivativefree optimization.
Our work leaves open several questions. For example, we have only dealt with bounds which hold in expectation, and our lower bounds focused on the dependence on , where other problem parameters, such as the Lipschitz constant and strong convexity parameter, are fixed constants. While this follows the setting of previous works, it does not cover situations where these parameters scale with . Finally, while this paper settles the case of stronglyconvex and smooth functions, we still don’t know what is the attainable performance for general convex functions, as well as the more specific case of stronglyconvex (possibly nonsmooth) functions. Our lower bound still holds, but the existing upper bounds are much larger: for convex functions, and for stronglyconvex functions (see table 1). We don’t know if the lower bound or the existing upper bounds are tight. However, it is the current upper bounds which seem less “natural”, and we suspect that they are the ones that can be considerably improved, using new algorithms which remain undiscovered.
We thank John Duchi, Satyen Kale, Robi Krauthgamer and the anonymous reviewers for helpful discussions and comments.
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Appendix A Improved Results for Quadratic Functions
In Sec. 3, we showed a tight bound on the achievable error for quadratic functions, in the derivativefree SCO setting. This was shown under the assumption that the noise is zeromean and has a second moment bounded by . In this appendix, we show how under additional natural assumptions on the noise, one can improve on this result with an efficient algorithm. The main message here is not so much the algorithmic result, but rather to show that the generic noise assumption is important for our lower bounds, and that better algorithms may still be possible for more specific settings.
To give a concrete example, consider the classic setting of ridge regression, where we have labeled training examples sampled i.i.d. from some distribution over , and our goal is to find some minimizing
In a bandit / derivativefree SCO setting, we can think of each query as giving as the value of
(6) 
for some specific example , and note that its expected value (over the random draw of ) equals . Thus, it falls within the setting considered in this paper. However, the noise process is not generic, but has a particular structure. We will show here that one can actually attain an error rate as good as for this problem.
To formally present our result, it would be useful to consider a more general setting, the ridge regression setting above being a special case. Suppose we can write as , where decomposes into a deterministic term and a stochastic quadratic term :
where are random variables. We assume that whenever we query a point , we get for some random realization of . In general, can be a stronglyconvex regularization term, such as in Eq. (6).
The algorithm we consider, Algorithm 2, is a slight variant of Algorithm 1, which takes this decomposition of into account when constructing its unbiased gradient estimate. Compared to Algorithm 1, this algorithm also queries at random points further away from , up to a distance of . We will assume here that we can always query at such points^{3}^{3}3Similar to Algorithm 1, if one can only query at some distance , where , then one can modify the algorithm to handle such cases, with the resulting error bound depending on .. We also let in the algorithm, where we recall that is some known upper bound on .
We now show that with this algorithm, one can improve on our error upper bound from (Thm. 3.1). In the setting described above, suppose are all at most with probability , the optimum has a norm of at most , and for any . Then under Assumption 1, the point returned by Algorithm 2 satisfies
where is the Frobenius norm. Note that if we only assume , then can be as high as , which leads to an bound, same as in Thm. 3.1. However, it may be much smaller than that. In particular, for the ridge regression case we considered earlier, corresponds to where is a randomly drawn instance. Under the common assumption that (independent of the dimension), it follows that . Therefore, is independent of the dimension, leading to an error upper bound in terms of .
We remark that even in this specific setting, the bound does not carry over to the bandit SCO setting (i.e. in terms of regret), since the algorithm requires us to query far away from . Also, we again emphasize that this result does not contradict our lower bound in the quadratic case (Thm. 3.2), since the setting there included a generic noise term, while here the stochastic “noise” has a very specific structure.
As to the proof of Thm. A, it is very similar to that of Thm. 3.1, the key difference being a better moment upper bound on the gradient estimate , as formalized in the following lemma. Plugging this improved bound into the calculations results in the theorem. For any , we have that is a subgradient of , and
By definition of , we note that
Using a similar calculation to the one in the proof of Lemma 3.1, we have that the expected value of this expression over and is
which is a subgradient of . As to the moment bound, we have
(7) 
Letting denote entry in , and recalling that by definition of , , we have that
Also, using the fact that
is the identity matrix, we have
Finally, we have
Plugging these inequalities back into Eq. (7), we get that
from which the lemma follows.
Appendix B Additional Proofs
b.1 Proof of Lemma 3.1
By the way is picked, we have that and that for all . Thus, letting denote expectation w.r.t. and the random function values, we have
Also, by the assumptions on and the assumptions on the noise , we have
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