1 Introduction
In the procedure of Principal Component Analysis (PCA) we aim at learning the principal leading eigenvector of the covariance matrix of a
dimensional random vector
from its independent and identically distributed realizations . Let, and let the eigenvalues of
be , then the PCA problem can be formulated as minimizing the expectation of a nonconvex function:(1) 
where denotes the Euclidean norm. Since the eigengap is nonzero, the solution to (1) is unique, denoted by . The classical method of finding the estimator of the first leading eigenvector can be formulated as the solution to the empirical covariance problem as
In words, denotes the empirical covariance matrix for the first samples. The estimator produced via this process provides a statistical optimal solution . Precisely, [43] shows that the angle between any estimator that is a function of the first samples and has the following minimax lower bound
(2) 
where is some positive constant. Here the infimum of is taken over all principal eigenvector estimators, and is the collection of all dimensional subgaussian distributions with mean zero and eigengap satisfying Classical PCA method has time complexity and space complexity . The drawback of this method is that, when the data samples are highdimensional, computing and storage of a large empirical covariance matrix can be costly.
In this paper we concentrate on the streaming or online method for PCA that processes online data and estimates the principal component sequentially without explicitly computing and storing the empirical covariance matrix . Over thirty years ago, Oja [30] proposed an online PCA iteration that can be regarded as a projected stochastic gradient descent method as
(3) 
Here is some positive learning rule or stepsize, and is defined as for each nonzero vector , namely, projects any vector onto the unit sphere . Oja’s iteration enjoys a less expensive time complexity and space complexity and thereby has been used as an alternative method for PCA when both the dimension and number of samples are large.
In this paper, we adopt the diffusion approximation method to characterize the stochastic algorithm using Markov processes and its differential equation approximations. The diffusion process approximation is a fundamental and powerful analytic tool for analyzing complicated stochastic process. By leveraging the tool of weak convergence, we are able to conduct a heuristic finitesample analysis of the Oja’s iteration and obtain a convergence rate which, by carefully choosing the stepsize
, matches the PCA minimax information lower bound. Our analysis involves the weak convergence theory for Markov processes [11], which is believed to have a potential for a broader class of stochastic algorithms for nonconvex optimization, such as tensor decomposition, phase retrieval, matrix completion, neural network, etc.
Our Contributions
We provide a Markov chain characterization of the stochastic process
generated by the Oja’s iteration with constant stepsize. We show that upon appropriate scalings, the iterates as a Markov process weakly converges to the solution of an ordinary differential equation system, which is a multidimensional analogue to the logistic equations. Also locally around the neighborhood of a stationary point, upon a different scaling the process weakly converges to the multidimensional OrnsteinUhlenbeck processes. Moreover, we identify from differential equation approximations that the global convergence dynamics of the Oja’s iteration has three distinct phases:

[label=()]

The initial phase corresponds to escaping from unstable stationary points;

The second phase corresponds to fast deterministic crossing period;

The third phase corresponds to stable oscillation around the true principal component.
Lastly, this is the first work that analyze the global rate of convergence analysis of Oja’s iteration, i.e., the convergence rate does not have any initialization requirements.
Related Literatures
This paper is a natural companion to paper by the authors’ recent work [23] that gives explicit rate analysis using a discretetime martingalebased approach. In this paper, we provide a much simpler and more insightful heuristic analysis based on diffusion approximation method under the additional assumption of bounded samples.
The idea of stochastic approximation for PCA problem can be traced back to Krasulina [19] published almost fifty years ago. His work proposed an algorithm that is regarded as the stochastic gradient descent method for the Rayleigh quotient. In contrast, Oja’s iteration can be regarded as a projected stochastic gradient descent method. The method of using differential equation tools for PCA appeared in the first papers [31, 19] to prove convergence result to the principal component, among which, [31] also analyze the subspace learning for PCA. See also [16, Chap. 1] for a gradient flow dynamical system perspective of Oja’s iteration.
The convergence rate analysis of the online PCA iteration has been very few until the recent big data tsunami, when the need to handle massive amounts of data emerges. Recent works by [6, 10, 34, 17] study the convergence of online PCA from different perspectives, and obtain some useful rate results. Our analysis using the tools of diffusion approximations suggests a rate that is sharper than all existing results, and our global convergence rate result poses no requirement for initialization.
More Literatures
Our work is related to a very recent line of work [13, 38, 39, 40, 41, 21, 3, 33] on the global dynamics of nonconvex optimization with statistical structures. These works carefully characterize the global geometry of the objective functions, and in special, around the unstable stationary points including saddle points and local maximizers. To solve the optimization problem various algorithms were used, including (stochastic) gradient method with random initialization or noise injection as well as variants of Newton’s method. The unstable stationary points can hence be avoided, enabling the global convergence to desirable local minimizers.
Our diffusion processbased characterization of SGD is also related to another line of work [8, 10, 37, 24, 26]. Among them, [10] uses techniques based on martingales in discrete time to quantify the global convergence of SGD on matrix decomposition problems. In comparison, our techniques are based on Stroock and Varadhan’s weak convergence of Markov chains to diffusion processes, which yield the continuoustime dynamics of SGD. The rest of these results mostly focus on analyzing continuoustime dynamics of gradient descent or SGD on convex optimization problems. In comparison, we are the first to characterize the global dynamics for nonconvex statistical optimization. In particular, the first and second phases of our characterization, especially the unstable OrnsteinUhlenbeck process, are unique to nonconvex problems. Also, it is worth noting that, using the arguments of [26], we can show that the diffusion processbased characterization admits a variational Bayesian interpretation of nonconvex statistical optimization. However, we do not pursue this direction in this paper.
In the mathematical programming and statistics communities, the computational and statistical aspects of PCA are often studied separately. From the statistical perspective, recent developments have focused on estimating principal components for very highdimensional data. When the data dimension is much larger than the sample size, i.e.,
, classical method using decomposition of the empirical convariance matrix produces inconsistent estimates [18, 29]. Sparsitybased methods have been studied, such as the truncated power method studied by [45] and [44]. Other sparsity regularization methods for high dimensional PCA has been studied in [18, 43, 42, 46, 9, 2, 25, 7], etc. Note that in this paper we do not consider the highdimensional regime and sparsity regularization.From the computational perspective, power iterations or the Lanczos method are well studied. These iterative methods require performing multiple products between vectors and empirical covariance matrices. Such operation usually involves multiple passes over the data, whose complexity may scale with the eigengap and dimensions [20, 28]. Recently, randomized algorithms have been developed to reduce the computation complexity [36, 35, 12]. A critical trend today is to combine the computational and statistical aspects and to develop algorithmic estimator that admits fast computation as well as good estimation properties. Related literatures include [4, 5, 27, 14, 10].
Organization
§2 introduces the settings and distributional assumptions. §3 briefly discusses the Oja’s iteration from the Markov processes perspective and characterizes that it globally admits ordinary differential equation approximation upon appropriate scaling, and also stochastic differential equation approximation locally in the neighborhood of each stationary point. §4 utilizes the weak convergence results and provides a threephase argument for the global convergence rate analysis, which is nearoptimal for the Oja’s iteration. Concluding remarks are provided in §5.
2 Settings
In this section, we present the basic settings for the Oja’s iteration. The algorithm maintains a running estimate of the true principal component , and updates it while receiving streaming samples from exterior data source. We summarize our distributional assumptions.
The random vectors are independent and identically distributed and have the following properties:

[label=()]

and ;

;

There is a constant such that .
For the easiness of presentation, we transform the iterates and define the rescaled samples, as follows. First we let the eigendecomposition of the covariance matrix be
where is a diagonal matrix with diagonal entries , and
is an orthogonal matrix consisting of column eigenvectors of
. Clearly the first column of is equal to the principal component . Note that the diagonal decomposition might not be unique, in which case we work with an arbitrary one. Second, let(4) 
One can easily verify that
The principal component of the rescaled random variable
, which we denote by , is equal to , where is the canonical basis of . By applying the orthonormal transformation to the stochastic process , we obtain an iterative process in the rescaled space:(5) 
Moreover, the angle processes associated with and are equivalent, i.e.,
(6) 
Therefore it would be sufficient to study the rescaled iteration in (5) and the transformed iteration throughout the rest of this paper.
3 A Theory of Diffusion Approximation for PCA
In this section we show that the stochastic iterates generated by the Oja’s iteration can be approximated by the solution of an ODE system upon appropriate scaling, as long as is small. To work on the approximation we first observe that the iteration , generated by (5) forms a discretetime, timehomogeneous Markov process that takes values on . Furthermore, holds strong Markov property.
3.1 Global ODE Approximation
To state our results on differential equation approximations, let us define a new process, which is obtained by rescaling the time index according to the stepsize
(7) 
We add the superscript in the notation to emphasize the dependence of the process on . We will show that converges weakly to a deterministic function , as .
Furthermore, we can identify the limit as the closedform solution to an ODE system. Under Assumption 2 and using an infinitesimal generator analysis we have
It follows that, as
, the infinitesimal conditional variance tends to 0:
and the infinitesimal mean is
Using the classical weak convergence to diffusion argument [11, Corollary 4.2 in §7.4], we obtain the following result.
If converges weakly to some constant vector as then the Markov process converges weakly to the solution to the following ordinary differential equation system
(8) 
with initial values .
We can straightforwardly check for sanity that the solution vector lies on the unit sphere , i.e., for all . Written in coordinates , the ODE is expressed for
One can straightforwardly verify that the solution to (8) has
(9) 
where is the normalization function
To understand the limit function given by (9), we note that in the special case where
and
(10) 
This is the formula of the logistic curve. Hence analogously, in (9) is namely the generalized logistic curves.
3.2 Local Approximation by Diffusion Processes
The weak convergence to ODE theorem introduced in §3.1 characterizes the global dynamics of the Oja’s iteration. Such approximation explains many behaviors, but neglected the presence of noise that plays a role in the algorithm. In this section we aim at understanding the Oja’s iteration via stochastic differential equations (SDE). We refer the readers to [32] for more on basic concepts of SDE.
In this section, we instead show that under some scaling, the process admits an approximation of multidimensional OrnsteinUhlenbeck process within a neighborhood of each of the unstable stationary points, both stable and unstable. Afterwards, we develop some weak convergence results to give a rough estimate on the rate of convergence of the Oja’s iteration. For purposes of illustration and brevity, we restrict ourselves to the case of starting point being the stationary point for some , and denote an arbitrary vector to be a dimensional vector that keeps all but the th coordinate of . Using theory from [11] we conclude the following theorem.
Let be arbitrary. If converges weakly to some as , then the Markov process
converges weakly to the solution of the multidimensional stochastic differential equation
(11) 
with initial values . Here is a standard dimensional Brownian motion. ^{1}^{1}1 The reason we have a dimensional OrnsteinUhlenbeck process is because the objective function of PCA is defined on a dimensional manifold and has independent variables.
The solution to (11) can be solved explicitly. We let for a matrix the matrix exponentiation as Also, let for the positive semidefinite diagonal matrix . The solution to (11) is hence
which is known as the multidimensional OrnsteinUhlenbeck process, whose behavior depends on the matrix and is discussed in details in §4.
Before concluding this section, we emphasize that the weak convergence to diffusions results in §3.1 and §3.2 should be distinguished from the convergence of the Oja’s iteration. From a random process theoretical perspective, the former one treats the weak convergence of finite dimensional distributions of a sequence of rescaled processes as tends to 0, while the latter one charaterizes the longtime behavior of a single realization of iterates generated by algorithm for a fixed .
4 Global ThreePhase Analysis of Oja’s Iteration
Previously §3.1 and §3.2 develop the tools of weak convergence to diffusion under global and local scalings. In this section, we apply these tools to analyze the dynamics of online PCA iteration in three phases in sequel. For purposes of illustration and brevity, we restrict ourselves to the case of starting point that is near a saddle point . Let denotes , a.s., and when both and hold.
4.1 Phase I: Noise Initialization
In consideration of global convergence, we analyze the initial phase where the iteration starts at a point on or around and eventually escapes an neighborhood of the set
When thinking the sphere as the globe with being the north and south poles, corresponds to the equator of the globe. Therefore, all unstable stationary points (including saddle points and local maximizers) lie on the equator .
4.2 Phase II: Deterministic Crossing
In Phase II, the iteration escapes from the neighborhood of equator and converges to a basin of attraction of the local minimizer . From strong Markov property of the Oja’s iteration introduced in the beginning of §3, one can forget the iteration steps in Phase I and analyze the iteration from the final iterate of Phase I. Suppose we have an initial point that satisfies , where is a fixed constant in , Theorem 3.1 concludes that the iteration moves in a deterministic pattern and quickly evolves into a small neighborhood of the principal component such that .
4.3 Phase III: Convergence to Principal Component
In Phase III, the iteration quickly converges to and fluctuates around the true principal component . We start our iteration from a neighborhood around the principal component, where has . Letting in (11) and taking the limit , we have the limit Rescaling the Markov process along with some calculations gives as , in very rough sense,
(12) 
The above display implies that there will be some nondiminishing fluctuations, variance being proportional to the constant stepsize , as time goes to infinity or at stationarity. Therefore in terms of angle, at stationarity the Markov process concentrates within a radius neighborhood of zero.
4.4 Crossing Time Estimate
We turn to estimate the running time, namely the crossing time, which is the number of iterates required for the iteration to cross the corresponding regions in different phases. We will use the relation to bridge the discretetime algorithm and its continuoustime approximation.
Phase I. For illustrative purposes we only consider the special case where is close to the th coordinate vector, which is a saddle point that has a negative Hessian eigenvalue. In this situation, the SDE (11) in terms of the first coordinate of reduces to
(13) 
with initial value . Solution to (13) is known as unstable OrnsteinUhlenbeck process [1] and can be expressed explicitly in closedform, as
Rescaling the time back to the discretetime iteration, we let and obtain
(14) 
In (14), the term is approximately distributed as
where stands for a standard normal variable. We have
(15) 
In order to have in (15), we have as the crossing time is approximately
(16) 
Therefore we have whenever the smallest eigenvalue is bounded away from 0, then asymptotically This suggests that the noise helps the iteration to move away from rapidly.
Phase II. We turn to estimate the crossing time in Phase II. (9) together with simple calculation ensures the existence of a constant , that depends only on such that . Furthermore has the following bounds:
(17) 
Translating back to the timescale of the iteration, it takes asymptotically
iterates to achieve . Theorem 3.1 indicates that when is positively small, the iterates needed for the first coordinate squared to cross from to is . This is substantiated by simulation results [4] suggesting that the Oja’s iteration moves fast from the warm initialization.
Phase III. To estimate the crossing time or the number of iterates needed in Phase III, we restart our counter and have from the approximation in Theorem 3.2 and (11) that
In terms of the iterations , note the relationship The end of Phase II implies that , and hence by setting
we conclude that as
(18) 
4.5 FiniteSample Rate Bound
In this subsection we establish the global finitesample convergence rate using the crossing time estimates in the previous subsection. Starting from where is arbitrary, the global convergence time as such that, by choosing as a small fixed constant,
with the following estimation on global convergence rate as in (12)
Given a fixed number of samples , by choosing as
(19) 
we have . Plugging in as in (19) we have, by the anglepreserving property of coordinate transformation (6), that
(20) 
The finite sample bound in (20) is sharper than any existing results and matches the information lower bound. Moreover, (20) implies that the rate in terms of sinesquared angle is which matches the minimax information lower bound (up to a factor), see for example, Theorem 3.1 of [43]. Limited by space, details about the rate comparison is provided in the supplementary material.
5 Concluding Remarks
We make several concluding remarks on the global convergence rate estimations, as follows.
Crossing Time Comparison. From the crossing time estimates in (16), (17), (18) we conclude

[label=()]

As we have . This implies that the algorithm demonstrates the cutoff phenomenon which frequently occur in discretetime Markov processes [22]. In words, the Phase II where the objective value in Rayleigh quotient drops from to is an asymptotically a phase of short time, compared to Phases I and III, so the convergence curve occurs instead of an exponentially decaying curve.

As we have . This suggests that for the high case that Phase I of escaping from the equator consumes roughly the same iterations as in Phase III.
To summarize from above, the cold initialization iteration roughly takes twice the number of steps than the warm initialization version which is consistent with the simulation discussions in [31].
Subspace Learning. In this work we primarily concentrates on the problem of finding the top1 eigenvector. It is believed that the problem of finding top eigenvectors, a.k.a. the subspace PCA problem, can be analyzed using our approximation methods. This will involve a careful characterization of subspace angles and is hence more complex. We leave this for future investigations.
References
 Aldous [1989] Aldous, D. (1989). Probability Approximations via the Poisson Clumping Heuristic, volume 77. Springer.
 Amini & Wainwright [2009] Amini, A. & Wainwright, M. (2009). Highdimensional analysis of semidefinite relaxations for sparse principal components. The Annals of Statistics, 37(5B), 2877–2921.
 Anandkumar & Ge [2016] Anandkumar, A. & Ge, R. (2016). Efficient approaches for escaping higher order saddle points in nonconvex optimization. arXiv preprint arXiv:1602.05908.
 Arora et al. [2012] Arora, R., Cotter, A., Livescu, K., & Srebro, N. (2012). Stochastic optimization for PCA and PLS. In 50th Annual Allerton Conference on Communication, Control, and Computing (pp. 861–868).
 Arora et al. [2013] Arora, R., Cotter, A., & Srebro, N. (2013). Stochastic optimization of PCA with capped msg. In Advances in Neural Information Processing Systems (pp. 1815–1823).
 Balsubramani et al. [2013] Balsubramani, A., Dasgupta, S., & Freund, Y. (2013). The fast convergence of incremental PCA. In Advances in Neural Information Processing Systems (pp. 3174–3182).
 Cai et al. [2013] Cai, T. T., Ma, Z., & Wu, Y. (2013). Sparse PCA: Optimal rates and adaptive estimation. The Annals of Statistics, 41(6), 3074–3110.
 Darken & Moody [1991] Darken, C. & Moody, J. (1991). Towards faster stochastic gradient search. In Advances in Neural Information Processing Systems (pp. 1009–1016).

d’Aspremont et al. [2008]
d’Aspremont, A., Bach, F., & El Ghaoui, L. (2008).
Optimal solutions for sparse principal component analysis.
Journal of Machine Learning Research
, 9, 1269–1294.  De Sa et al. [2015] De Sa, C., Olukotun, K., & Ré, C. (2015). Global convergence of stochastic gradient descent for some nonconvex matrix problems. In Proceedings of the 32nd International Conference on Machine Learning (ICML15) (pp. 2332–2341).
 Ethier & Kurtz [2005] Ethier, S. N. & Kurtz, T. G. (2005). Markov Processes: Characterization and Convergence, volume 282. John Wiley & Sons.
 Garber & Hazan [2015] Garber, D. & Hazan, E. (2015). Fast and simple PCA via convex optimization. arXiv preprint arXiv:1509.05647.
 Ge et al. [2015] Ge, R., Huang, F., Jin, C., & Yuan, Y. (2015). Escaping from saddle points – online stochastic gradient for tensor decomposition. In Proceedings of The 28th Conference on Learning Theory (pp. 797–842).
 Hardt & Price [2014] Hardt, M. & Price, E. (2014). The noisy power method: A meta algorithm with applications. In Advances in Neural Information Processing Systems (pp. 2861–2869).
 Hardt, Moritz & Price, Eric [2014] Hardt, Moritz & Price, Eric (2014). The Noisy Power Method: A Meta Algorithm with Applications. NIPS, (pp. 2861–2869).
 Helmke & Moore [1994] Helmke, U. & Moore, J. B. (1994). Optimization and Dynamical Systems. Springer.
 Jain et al. [2016] Jain, P., Jin, C., Kakade, S. M., Netrapalli, P., & Sidford, A. (2016). Matching matrix bernstein with little memory: Nearoptimal finite sample guarantees for oja’s algorithm. arXiv preprint arXiv:1602.06929.
 Johnstone & Lu [2009] Johnstone, I. M. & Lu, A. Y. (2009). On Consistency and Sparsity for Principal Components Analysis in High Dimensions. Journal of the American Statistical Association, 104(486), 682–693.
 Krasulina [1969] Krasulina, T. (1969). The method of stochastic approximation for the determination of the least eigenvalue of a symmetrical matrix. USSR Computational Mathematics and Mathematical Physics, 9(6), 189–195.
 Kuczynski & Wozniakowski [1992] Kuczynski, J. & Wozniakowski, H. (1992). Estimating the largest eigenvalue by the power and lanczos algorithms with a random start. SIAM journal on matrix analysis and applications, 13(4), 1094–1122.
 Lee et al. [2016] Lee, J. D., Simchowitz, M., Jordan, M. I., & Recht, B. (2016). Gradient descent only converges to minimizers. In Conference on Learning Theory (pp. 1246–1257).
 Levin et al. [2009] Levin, D. A., Peres, Y., & Wilmer, E. L. (2009). Markov chains and mixing times. American Mathematical Society.
 Li et al. [2016] Li, C. J., Wang, M., Liu, H., & Zhang, T. (2016). Nearoptimal stochastic approximation for online principal component estimation. arXiv preprint arXiv:1603.05305.
 Li et al. [2015] Li, Q., Tai, C., & E, W. (2015). Dynamics of stochastic gradient algorithms. arXiv preprint arXiv:1511.06251.
 Ma [2013] Ma, Z. (2013). Sparse principal component analysis and iterative thresholding. The Annals of Statistics, 41(2), 772–801.
 Mandt et al. [2016] Mandt, S., Hoffman, M. D., & Blei, D. M. (2016). A variational analysis of stochastic gradient algorithms. arXiv preprint arXiv:1602.02666.
 Mitliagkas et al. [2013] Mitliagkas, I., Caramanis, C., & Jain, P. (2013). Memory limited, streaming PCA. In Advances in Neural Information Processing Systems (pp. 2886–2894).
 Musco & Musco [2015] Musco, C. & Musco, C. (2015). Stronger approximate singular value decomposition via the block lanczos and power methods. arXiv preprint arXiv:1504.05477.
 Nadler [2008] Nadler, B. (2008). Finite sample approximation results for principal component analysis: A matrix perturbation approach. The Annals of Statistics, 41(2), 2791–2817.

Oja [1982]
Oja, E. (1982).
Simplified neuron model as a principal component analyzer.
Journal of mathematical biology, 15(3), 267–273. 
Oja & Karhunen [1985]
Oja, E. & Karhunen, J. (1985).
On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix.
Journal of mathematical analysis and applications, 106(1), 69–84.  Oksendal [2003] Oksendal, B. (2003). Stochastic Differential Equations. Springer.
 Panageas & Piliouras [2016] Panageas, I. & Piliouras, G. (2016). Gradient descent converges to minimizers: The case of nonisolated critical points. arXiv preprint arXiv:1605.00405.
 Shamir [2015a] Shamir, O. (2015a). Convergence of stochastic gradient descent for PCA. arXiv preprint arXiv:1509.09002.
 Shamir [2015b] Shamir, O. (2015b). Fast stochastic algorithms for svd and PCA: Convergence properties and convexity. arXiv preprint arXiv:1507.08788.
 Shamir [2015c] Shamir, O. (2015c). A stochastic PCA and svd algorithm with an exponential convergence rate. In Proceedings of the 32nd International Conference on Machine Learning (ICML15) (pp. 144–152).
 Su et al. [2016] Su, W., Boyd, S., & Candes, E. J. (2016). A differential equation for modeling Nesterov’s accelerated gradient method: theory and insights. Journal of Machine Learning Research, 17(153), 1–43.
 Sun et al. [2015a] Sun, J., Qu, Q., & Wright, J. (2015a). Complete dictionary recovery over the sphere i: Overview and the geometric picture. arXiv preprint arXiv:1511.03607.
 Sun et al. [2015b] Sun, J., Qu, Q., & Wright, J. (2015b). Complete dictionary recovery over the sphere ii: Recovery by Riemannian trustregion method. arXiv preprint arXiv:1511.04777.
 Sun et al. [2015c] Sun, J., Qu, Q., & Wright, J. (2015c). When are nonconvex problems not scary? arXiv preprint arXiv:1510.06096.
 Sun et al. [2016] Sun, J., Qu, Q., & Wright, J. (2016). A geometric analysis of phase retrieval. arXiv preprint arXiv:1602.06664.
 Vu & Lei [2012] Vu, V. Q. & Lei, J. (2012). Minimax Rates of Estimation for Sparse PCA in High Dimensions. AISTATS, (pp. 1278–1286).
 Vu & Lei [2013] Vu, V. Q. & Lei, J. (2013). Minimax sparse principal subspace estimation in high dimensions. The Annals of Statistics, 41(6), 2905–2947.
 Wang et al. [2014] Wang, Z., Lu, H., & Liu, H. (2014). Nonconvex statistical optimization: Minimaxoptimal sparse pca in polynomial time. arXiv preprint arXiv:1408.5352.
 Yuan & Zhang [2013] Yuan, X.T. & Zhang, T. (2013). Truncated power method for sparse eigenvalue problems. Journal of Machine Learning Research, 14(Apr), 899–925.
 Zou [2006] Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American statistical association, 101(476), 1418–1429.
Appendix A Proofs of Auxiliary Results
This section provides the proofs of auxilary Propositions. For brevity, we use the following notations throughout Section A of the Appendix: (i) The ’s with subscripts denotes some positive numerical constants; (ii) The , , ’s (without subscripts) are positive numerical constants whose values may change between lines; (iii) The and ; (iv) For generic function the .
Also in this section, we let be the filtration of the algorithm iterates, i.e. the field generated by the stochastic iterates by .
a.1 Analysis of Algorithm
To analyze the algorithm from the view of a Markov chain, we need to understand the increments on each coordinate at each step. Under Assumption 2, for each and we have for all the following:

[label=()]

There exists a random variable with almost surely, such that the increment on coordinate at iterate can be represented as
(21) 
The increment has the following bound
(22) 
There exists a deterministic function with
such that for all ,
(23)
To prove Proposition A.1 we first come to show
For each
Proof.
Since
(24) 
Taylor expansion suggests for
which is an alternating series for , whereas the absolute terms approach to 0 monotonically
Hence the error bound gives
(25) 
Noting we have for all
The above display is strictly less than 1 when , and hence (25) applies. Combined with (24) we have
Noticing , triangle inequality suggests
completing the proof.
∎
Proof of Proposition a.1.
Setting . Then
where
(26) 
Note the term
is absolutely bounded by , and taking expectation gives