Convergence and complexity of block coordinate descent with diminishing radius for nonconvex optimization

Block coordinate descent (BCD), also known as nonlinear Gauss-Seidel, is a simple iterative algorithm for nonconvex optimization that sequentially minimizes the objective function in each block coordinate while the other coordinates are held fixed. We propose a version of BCD that, for block multi-convex and smooth objective functions under constraints, is guaranteed to converge to the stationary points with worst-case rate of convergence of $O((\log n)^{2}/n)$ for $n$ iterations, and a bound of $O(\epsilon^{-1}(\log \epsilon^{-1})^{2})$ for the number of iterations to achieve an $\epsilon$-approximate stationary point. Furthermore, we show that these results continue to hold even when the convex sub-problems are inexactly solved if the optimality gaps are uniformly summable against initialization. A key idea is to restrict the parameter search within a diminishing radius to promote stability of iterates. As an application, we provide an alternating least squares algorithm with diminishing radius for nonnegative CP tensor decomposition that converges to the stationary points of the reconstruction error with the same robust worst-case convergence rate and complexity bounds. We also experimentally validate our results with both synthetic and real-world data and demonstrate that using auxiliary search radius restriction can in fact improve the rate of convergence.


INTRODUCTION
Throughout this paper, we are interested in the minimization of a continuous function f : R I 1 ×· · ·× R I m → [0, ∞) on a cartesian product of convex sets Θ = Θ (1) × · · · × Θ (m) : θ * ∈ arg min θ=[θ 1 ,...,θ m ]∈Θ f (θ 1 , . . . , θ m ). (1) We are interested in the case when f is block multi-convex, that is, when f is convex in each block coordinate θ i . When the objective function f is nonconvex, the convergence of any algorithm for solving (1) to a globally optimal solution can hardly be expected. Instead, global convergence to stationary points of the objective function is desired.
Block coordinate descent (BCD), also known as nonlinear Gauss-Seidel [Ber99,Wri15], is a simple iterative algorithm that sequentially minimizes the objective function in each block coordinate while the other coordinates are held fixed. Due to its simplicity, BCD has been widely used in various optimization problems such as nonnegative matrix or tensor factorization [LS99,LS01,KB09]. More precisely, fix an initial estimate θ 0 ∈ Θ, and obtain a new estimate θ n from the previous one θ n−1 by successively updating the i th block coordinate using the following rule for i = 1, . . . , m: Then (2) generates a sequence of esimates θ n = [θ (1) n , . . . , θ (m) n ] for n ≥ 1. Convergence properties of BCD for convex f has been extensively studied [H + 57, SS73,LT92,Tse91]. It is known that BCD does not always converge to the stationary points of the non-convex objective function that is convex in each block coordinate [Pow73], but such global convergenece is guaranteed under additional assumptions: Two-block (m = 2) or strict quasiconvexity for m − 2 blocks [GS99,GS00] and uniqueness of minimizer per block [Ber97,Sec. 2.7].
One way to guarantee convergence of BCD without requiring additional reguality assumptions is to use proximal regularization, which in the simplest setting takes the following form (see [GS00,XY13, The codes for the main algorithm and simulations are provided in https://github.com/HanbaekLyu/BCD-DR.
While there exists an extensive amount of work on the rate of convergence of BCD when the objective function f is convex on the whole domain, the rate of convergence in the general block multiconvex case is not very well understood. Namely, various sublinear bounds on the rate of convergence is known for convex problems assuming local error bounds [LT93], projected gradient descent [BT13], and randomized and accelerated coordinate descent [Nes12].
In this work, we introduce a new modification of the standard BCD (2) and achieve the following results: (1) Global convergence to stationary points for block multi-convex and differentiable objectives under constraints; (2) Worst-case rate of convergence to stationary points of order (log n) 2 /n; (3) Worst-case bound of O(ε −1 (log ε −1 ) 2 ) on the number of iterations to achieve ε-approximate stationary points; (4) Robustness of the aformentioned results under inexact execution of the algorithm.
To our best knowledge, we believe our work provides the first result on the global rate of convergence and iteration complexity of BCD for the general block multi-convex case, especially with the additional robustness result. We do not impose any additional assumptions such as the Kurdyka-Łojasiewicz property. For gradient descent methods with unconstrained nonconvex objective, it is known that such rate of convergence cannot be faster than O(1/n) [CGT10], so our rate bound matches the optimal result up to a (log n) 2 factor. We experimentally validate our results with both synthetic and real-world data and demonstrate that using auxiliary search radius restriction can in fact improve the rate of convergence over the standard BCD (2). However, we have not proved that our algorithm converges faster than the standard BCD in general.

3:
Update estimate θ n = [θ (1) n , · · · , θ (m) n ] by 4: For i = 1, · · · , m do: Our key idea is to use an additional (but rather natural) constraint of "diminishing radius", by which we mean restricting the range of parameter search within a diminishing radius from the previous estimation. Namely, instead of adding the proximal regularizer θ − θ (i ) n−1 2 F to the objective function, we impose an additional constraint θ − θ (i ) n−1 F ≤ c w n , where c > 0 is a fixed constant and (w n ) n≥1 is a non-increasing sequence of weights in (0, 1]. See Algorithm 1. As pointed out in [Ber99,Prop. 2.7.1], a necessary property that implies convergence of BCD to stationary points is the "stability of estimates", that the change made in each block update (2) vanishes. Our diminishing radius condition directly bakes such property in the algorithm in the form of an auxiliary constraint. Moreover, we remark that using proximal regularization in (3) only ensures square-summability of iterates, ∞ n=1 θ n − θ n−1 2 F < ∞ (see, e.g., [XY13, Lem 2.2]), while our diminishing radius constraint gives a finer control of individual iterates, θ n − θ n−1 F ≤ c w n . This advantage is crucial in obtaining the rate of convergence in our analysis. As a trade-off, we devote a substantial amount of work to show the auxiliary diminishing radius constraint vanishes along every convergent subsequence.

STATEMENT OF MAIN RESULTS
Throughout this paper, we assume the following two mild conditions: In (A1), the assumption that each Θ (i ) ⊆ R I i contains an open ball in R I i does not lose any generality. Indeed, otherwise, Θ (i ) is contained in some hyperplane and after a change of coordinates, we can reduce the full dimension of the ambient spcae R I i . The smoothness assumption is standard on the literature of BCD (see, e.g., [XY13,LX17]). It is important to note that under (A1)-(A2), the subproblem of block minimization (4) is a convex minimization problem over a convex set n F ≤ c w n+1 } (being the intersection of two convex sets), so Algorithm 1 can be readily executed using standard convex optimization procedures (see, e.g., [Ber99]). (We do cover the case when these convex sub-problems are only approximately solved in Theorem 2.2.) Recall that we say θ * ∈ Θ is a stationary point of f over Θ if where 〈·, ·〉 denotes the dot project on R I 1 +···+I m ⊇ Θ. This is equivalent to saying that −∇ f (θ * ) is in the normal cone of Θ at θ * . If θ * is in the interior of Θ, then it implies ∇ f (θ * ) F = 0. For iterative algorithms, such a first-order optimality condition may hardly be satisfied exactly in a finite number of iterations, so it is more important to know how the worst-case number of iterations required to achieve an ε-approximate solution scales with the desired precision ε. More precisely, we say θ * ∈ Θ is an ε-approxiate stationary point of f over Θ if This notion of ε-approximate solution is consistant with the corresponding notion for unconstrained problems. Indeed, if θ * is an interior point of Θ, then (5) reduces to ∇ f (θ * ) 2 F ≤ ε. Next, for each ε > 0 we define the worst-case iteration complexity N ε of an algorithm for solving (1) as where (θ n ) n≥0 is a sequence of estimates produced by the algorithm with initial estimate θ 0 . Note that N ε gives the worst-case bound on the number of iterations for an algorithm to achieve an εapproximate solution due to the supremum over the initialization θ 0 in (6). Now we state the first main result, Theorem 2.1. To our best knowledge, this gives the first worstcase rate of convergence and iteration complexity of BCD-type algorithms in the literature.
It is worth noting that, imposing an aditional assumption that all stationary points of f of Θ are isolated, then global convergence to a single stationary point can be easily deduced from Theorem 2.1 (i). However, requiring global convergence to a single stationary point in the general case (especially when the stationary point may accumulate) seems to be too strong. Indeed, the standard BCD does not warrant such a strong global convergence (e.g., when the objective function is globally minimized at every point in a small ball of factors). For our algorithm, θ n will converge to a single point trivially if the radii are summable (i.e., ∞ n=1 w n < ∞). However, in this case convergence to stationary points is not guaranteed in general, as all stationary points could be farther than the sum of radii from initialization.
Theorem 2.1 (ii) gives a worst-case rate of convergence of Algorithm 1. Namely, for each n ≥ 1, uniformly among all initialization θ 0 , there exists k ∈ {1, . . . , n} such that θ k is an ε-approximate solution with ε = c/ n i =1 w i . If we consider the weight w n = n −γ for some γ > 0, then the hypothesis of Theorem 2.1 requires that γ ∈ (1/2, 1], and the bound in (7) is optimized when γ 1/2. More precisely, consider the weights w n = 1/( n log n). Then Theorem 2.1 holds and we have for some constant c > 0 for all n ≥ 1. In particular, if all stationary points of f in Θ are in the interior of Θ, then (7) implies Hence we obtain the worst-case rate of convergence in gradient norm squared of order O((log n) 2 /n). We remark that in the literature of stochastic gradient descent for unconstrained problems, the rate of convergence is often measured by the worst-case gradient norm squared [SSY18, XYY + 19, WWB19,KW18]. Hence we can interpret (8) as giving a worst-case rate of convergence of order O((log n) 2 /n) for constrained nonconvex problems.
Our second set of results in this work considers robustness of our former result in Theorem 2.1 when the convex sub-problems in Algorithm 1 are solved only approximately. We show that Theorem 2.1 continues to hold under inexact compution of (4) if the errors we make in solving each convex sub-problem are "uniformly summable". To make a precise statement, we introduce the following notion of "optimality gap". Let (θ n ) n≥0 be an inexct output of Algorithm 1. Writeθ n = [θ (1) n , . . . ,θ (m) n ] for n ≥ 1 and denote the i th block-marginal cost function for i = 1, . . . , m and n ≥ 1. Eachθ (i ) n would be a minimizer of f (i ) n over the convex set Θ (i ) if (θ n ) n≥0 were an exact output of Algorithm 1. Hence we define the optimality gap at iteration n as Theorem 2.2. Assume (A1)-(A2). Suppose weights (w n ) n≥1 satisfy ∞ n=1 w 2 n < ∞. Let (θ n ) n≥0 be an inexct output of Algorithm 1. Then the following hold: F , thenθ n converges to the set of stationary points of f over Θ.
Note that since the sub-problems (4) are convex so that they can be solved approximately up to arbitrary prescribed precision. Hence summability of the optimality gap can easily be satisfied. For instance, the optimality gap shrinks linearly fast if the sub-problems (4) are strictly convex by using coordinate descent algorithms or using gradient descent algorithms, where also superlinear convergence is available using accelerated methods under additional assumptions [Wri15,BCN18].
Lastly, we apply our method and main results to Nonnegative CANDECMP/PARAFAC Decomposition (NCPD) [SH05,Zaf09]. The usual Alternating Least Squares (ALS) algorithm for solving NCPD [KB09] is the vanila BCD (2) for NCPD, which does not have a general convergence guarantee when the number of modes exceeds two. With additional diminishing raidus condition, the ALS is modified as Algorithm 2, which now enjoys all theoretical convergence guarentees and bounds on the rate of convergence and complexity stated in Theorems 2.1. Moreover, by Theorem 2.2 these results are robust under inexact computation of each nonnegative least squares computation. These results are stated in Corollary 5.1 and are new are new to the literature on NCPD.
2.1. Orgnization. This paper is organized as follows. In Section 3, we prove Theorem 2.1. Modifying the arguments in the exact case, we prove Theorem 2.2 in Section 4. Then we apply our theory to NCPD and drive Corollary 5.1 in Section 5. Finally, we provide some experimental results for NCPD problems in Section 6.
We start by recalling a classical lemma on the first-order approximation of functions with Lipshitz gradients.
Lemma 3.1 (First-order approximation of functions with Lipschitz gradient). Let f : R p → R be differentiable and ∇ f be L-Lipschitz continuous. Then for each θ, θ ∈ R p , Proof. This is a classical lemma. See [Nes98, Lem 1.2.3].

Proposition 3.2 (Forward monotonicity). Suppose (A1) and (A2). Then f
Recall that θ (i ) n is a minimizer of the convex function f (i ) n over the convex set Θ (i ) n that contains θ (i ) n−1 (see (11)). Hence each summand in the last expression above is nonnegative. This shows f (θ n−1 ) − f (θ n ) ≥ 0, as desired.

Remark 3.4. Proposition 3.3 still holds if we replace the assumption (A1) with uniform boundedness of f over Θ.
The following is a simple observation based on (A1).
Proof. Let R min denote the minimum radius of all open balls in Θ (i ) for i = 1, . . . , m (recall (A1)). We will show the assertion holds for Next, we show that the block coordinate descent we use to obtain θ n+1 should always give the optimal first-order descent up to a small additive error. Proposition 3.6 (Asymptotic first-order optimality). Assume (A1)-(A2) and w n = o(1). Fix a sequence (b n ) n≥1 such that 0 < b n ≤ w n for all n ≥ 1. Then there exists constant c 1 > 0 that do not depend on θ 0 ∈ Θ such that for all n ≥ 1, Proof. Let R 0 > 0 denote the constant as in Proposition 3.5. Since w n = o(1), there exists N 0 ≥ 1 such that c w n ≤ R 0 for all n ≥ N 0 . Such N 0 does not depend on θ 0 . We will prove the assertion for all n ≥ N 0 . Since all quantities in the assertion up to iteration N 0 are uniformly bounded (using compactness in (A1)), the assertion for all n ≥ 1 then follows. Recall which holds for all θ ∈ Θ with θ − θ n F = c b n+1 . Adding up these inequalities for i = 1, . . . , m, we get Noting that Θ is convex by (A1), we see that (13) also holds for all θ ∈ Θ with θ − θ n F ≥ c b n+1 . Indeed, for such θ, let θ be the point in the secant line between θ and θ n such that θ −θ n F ≥ c b n+1 . Then θ ∈ Θ and the above inequality holds for θ replaced with θ . However, the right hand side is unchanged when replacing θ with any point on the line passing through θ and θ n .
To finish the argument, it remains to argue that (13) holds for all θ ∈ Θ for all n ≥ N 0 with possibly different constant c 1 > 0. Let ∂Θ denote the boundary of Θ. Since Θ is convex and (13) is invariant under radial scaling of the vector θ − θ n , it suffices to verify (13) for boundary points of Θ that are within c b n+1 from θ n . To this end, fix θ ∈ ∂Θ with θ −θ n F < c b n+1 . Using Proposition 3.5, choose θ n ∈ Θ such that θ n − θ n F = c b n+1 . Now by a change of coordinates, we can view the graph of the boundary ∂Θ near θ n as a convex function. Then by a Taylor expansion, there exists a constant c 2 > 0 independent of θ 0 ∈ Θ such that for all n ≥ 1, Note that the constant c 2 above can be chosen to be independent of θ . Since (13) holds for θ = θ and ∇ f is L-Lipschitz, combining (13) and (14) gives Since this holds for all θ ∈ ∂Θ with θ − θ n F < c b n+1 , this completes the proof.
Recall that during the update θ n−1 → θ n each block coordinate of θ n−1 changes by at most c w n in Frobenius norm. For each n ≥ 1, we say θ n is a long point if none of the block coordinates of θ n−1 change by c w n in Frobenius norm and a short point otherwise. Observe that if θ n is a long point, then imposing the search radius restriction in (4) has no effect and θ n is obtained from θ n−1 by a single cycle of block coordinate descent on f over Θ.
Proof. By Proposition 3.3, we have Hence the former condition implies the latter condition in (21). Thus it suffices to show that this latter condition implies the assertion. Assume this condition, and let (t k ) k≥1 be a subsequence of (n k ) k≥1 for which the liminf in (21) is achieved. By taking a further subsequence, we may assume that θ ∞ = lim k→∞ θ t k exists. Now suppose for contradiction that θ ∞ is not a stationary point of f over Θ. Then there exists θ ∈ Θ and δ > 0 such that Noting that θ n −θ n−1 F = O(w n ) = o(1), we see that the right hand side goes to zero as k → ∞. Hence for all sufficiently large k ≥ 1, we have Noting that θ n+1 − θ n F ≤ mc w n+1 , applying Proposition 3.6 with b n = θ n+1 − θ n F /mc gives which is a contradiction. This shows the assertion.
The following proposition is crucial in establishing global convergence to stationary poitns (Theorem 2.1 (i)) and it is the only place where we use non-summability of the weights w n in the proof of Theorem 2.1. Proof. Let A denote the event that there exists a non-stationary limit point θ ∞ of Λ. Let B denote the event that there exists an ε-neighborhood B ε (θ ∞ ) of θ ∞ that does not contain any long points of Λ. We claim that A implies B . Suppose for contrary that A \ B is true. Then for all ε > 0, B ε (θ ∞ ) contains some long point of Λ. Then there exists a sequence of long points of A converging to θ ∞ , but then θ ∞ has to be stationary by Proposition 3.7. This contradicts to A. Hence A implies B .
Lastly, from the earlier results, on the event A, we can choose ε > 0 such that B ε (θ ∞ ) has no long point of Λ and also satisfies (a). We will show that B ε/2 (θ ∞ ) satisfies (b). Then B ε/2 (θ ∞ ) satisfies (a)-(b), as desired. Suppose for contradiction there are only finitely many θ n 's outside of B ε/2 (θ ∞ ). Then there exists an integer M ≥ 1 such that θ n ∈ B ε/2 (θ ∞ ) for all t ≥ M . Then each θ n for t ≥ M is a short point of Λ. By definition, it follows that θ n −θ n−1 F ≥ c w n for all t ≥ M . Then since ∞ n=1 w n = ∞, by Proposition 3.8 there exists a subsequence (s k ) k≥1 such that θ ∞ := lim k→∞ θ n k exists and is stationary. But since θ ∞ ∈ B ε (θ), this contradicts (a) for B ε (θ). This shows the assertion.
Lemma 3.10. Let (a n ) n≥0 and (b n ) n≥0 be sequences of nonnegative real numbers such that ∞ n=0 a n b n < ∞. Then Proof. The assertion follows from noting that n k=1 a k min We are now ready to give a proof of Theorem 2.1.
Proof of Theorem 2.1. Assume (A1), (A2) and ∞ n=1 w 2 n < ∞. We first show (ii). First note that by Proposition 3.6 with b k = w k , we have for some constants c 1 , c 2 > 0 for all k ≥ 1. From Proposition 3.3 and since ∞ n=1 w 2 n < ∞, for some constant M > 0 that does not depend on the initialization θ 0 . Thus by Lemma 3.10, we have Hence by (17), we conclude Since M does not depend on θ 0 , we can take the supremum over θ 0 ∈ Θ on both sides above and finish the proof of (ii). Next, (iii) is a direct consequence of (ii). Indeed, if w n = 1/( n log n), then the uniform upper bound on the rate of convergence in (ii) is of order O((log n) 2 /n). Then one can conclude by using the fact that n ≥ 2ε −1 (log ε −1 ) 2 implies (log n) 2 /n ≤ ε for all sufficiently small ε > 0.
Finally, we assume ∞ n=1 w n = ∞ and show (i). Suppose for contradiction that there exists a nonstationary limit point θ ∞ ∈ Θ of Λ. By Proposition 3.9, we may choose ε > 0 such that B ε (θ ∞ ) satisfies the conditions (a)-(b) of Proposition 3.9. Choose M ≥ 1 large enough so that w n < ε/4 whenever t ≥ M . We call an integer interval I := [ , ] a crossing if θ ∈ B ε/3 (θ ∞ ), θ ∈ B 2ε/3 (θ ∞ ), and no proper subset of I satisfies both of these conditions. By definition, two distinct crossings have empty intersection. Fix a crossing I = [ , ], it follows that by triangle inequality, Note that since θ ∞ is a limit point of Λ, θ n visits B ε/3 (θ ∞ ) infinitely often. Moreover, by condition (a) of Proposition 3.9, θ n also exits B ε (θ ∞ ) infinitely often. It follows that there are infinitely many crossings. Let t k denote the k th smallest integer that appears in some crossing. Then t k → ∞ as k → ∞, and by (18), Then by Proposition 3.8, there exists a subsequence (s k ) k≥1 of (t k ) k≥1 such that θ ∞ := lim k→∞ θ s k exists and is a stationary of f over Θ. However, since θ t k ∈ B 2ε/3 (θ ∞ ) for all k ≥ 1, we have θ ∞ ∈ B ε (θ ∞ ). This contradicts the condition (b) of Proposition 3.9 for B ε (θ ∞ ) that it cannot contain any stationary point of f over Θ. This shows that there is no non-stationary limit point θ ∞ ∈ Θ of Λ.

PROOF OF THEOREM 2.2: THE INEXACT CASE
In this section, we extend our analysis in the main text to the case where the convex sub-problems (4) in the main algorithm (Algorithm 1) is solved inexactly, thereby establishing Theorem 2.2. Throughout this section, (θ n ) n≥1 is assumed to be an inexact solution of Algorithm 1 and (∆) n≥1 denote the associated optimality gap defined in (10). We writeθ n = [θ (1) n , . . . ,θ (m) n ] for each n ≥ 1 and let f (i ) n denote the i th block marginal cost function at iteration n as defined in (12). Also, we define Λ and Θ (i ) n similarly as in the beginning of Section 3. Namely, Λ := {θ n | t ≥ 1} ⊆ Θ and for each n ≥ 1 and i ∈ {1, . . . , m}, we denote In order to prove Theorem 2.2, we will proceed by making modifications of the statements in the main text we proved for the exact trajectory of Algorithm 1. We first need the following approximate monotonicity result.
Let θ (i ) n be a minimizer of the convex function θ → f (i ) n (θ) over the convex set Θ (i ) n . By the definition of the optimality gap ∆ n in (10), we have Hence we have Then the assertion follows by summing over the above inequality for i = 1, . . . , m.

Remark 4.2. We remark that f
n is an estimate of the minimizer of f (i ) n over Θ (i ) n , which is a ball in Θ (i ) centered atθ (i ) n−1 , this is an entirely trivial condition to satisfy, e.g., by using a line search in each iteration. With this additional assumption, the numerator M + 2m ∞ n=1 ∆ n in the upper bound in Theorem 2.2 can be reduced to M + m ∞ n=1 ∆ n , see the proof of Theorem 2.2.
Proof. According to (A1) and (A2), it follows that ∇ f overθ is Lipschitz with some uniform Lipshitz constant L > 0. Hence by Lemma 3.1, for all t ≥ 1, Using Proposition 4.1, it follows that for n ≥ 1. Note that θ n −θ n+1 Then we can conclude since the first two terms in the middle of the equations above can be uniformly bounded from above with respect to θ 0 .
Next, we show an analogue of Proposition 3.6 in the inexact case.
Then it follows that where the first inequality above follows by the definition of the optimality gap ∆ n+1 in (10). Write this as Recall is Lipschitz with uniform Lipschitz constant L > 0 by (A2). Hence by Lemma 3.1, we get where ∇ 1 denotes the partial gradient with respect to the first block-coordinate. Applying a similar argument for all other blocks, it follows that ∇ f (θ ∞ ), (θ −θ ∞ ) ≥ 0 for all θ ∈ Θ. This shows the assertion.
Hence the former condition implies the latter condition in (21). Thus it suffices to show that this latter condition implies the assertion. Assume this condition, and let (t k ) k≥1 be a subsequence of (n k ) k≥1 for which the liminf in (21) is achieved. By taking a further subsequence, we may assume that θ ∞ = lim k→∞θt k exists. Now suppose for contradiction thatθ ∞ is not a stationary point of f over Θ. Then there exists θ ∈ Θ and δ > 0 such that ∇ f (θ ∞ ), (θ −θ ∞ ) < −δ < 0. By triangle inequality, write Noting that θ n −θ n−1 F = O(w n ) = o(1), we see that the right hand side goes to zero as k → ∞. Hence for all sufficiently large k ≥ 1, we have ∇ f (θ t k ), (θ −θ t k ) < −δ /2.
Noting that θ n+1 −θ n F ≤ mc w n+1 , applying Proposition 4.4 with b n = θ n+1 −θ n F /mc , for some constants c 1 , c 2 > 0 for all n ≥ 1. Note that here we have used crucially the assumption The rest of the proof is similar to the proof of Proposition 3.8. Proof. Proof is similar to that of Proposition 3.9 using the inexact counterparts of the earlier results shown in this section.
We now prove Theorem 2.2.
Lastly, we assume ∞ n=1 w n = ∞ and show (i). For this, suppose ∆ n = O( θ n −θ n−1 2 F ). (Since θ n −θ n−1 F = O(w n ) and ∞ n=1 w 2 n < ∞, this is a stronger condition than ∞ n=1 ∆ n < ∞.) Under this assumption, the exact counterparts of Proposition 3.9, Proposition 4.7, holds. Then an identical argument as in the proof of Theorem 2.1 shows thatθ n converges to the set of all stationary points of f over Θ.

APPLICATIONS TO CONSTRAINED MATRIX AND TENSOR FACTORIZATION
In this section, we discuss applications of our main result in the setting of nonnegative matrix and tensor factorization problems [LS99,LS01,KB09]. Nonnegative matrix factorization (NMF) has found applications in text analysis, image reconstruction, medical imaging, bioinformatics, and many other scientific fields more generally [SGH02, BB05, BBL + 07, CWS + 11, TN12, BMB + 15, RPZ + 18]. As matrix factorization is for unimodal data, nonnegative tensor factorization (NTF) provides a powerful and versatile tool that can extract useful latent information out of multi-model data tensors. As a result, tensor factorization methods have witnessed increasing popularity and adoption in modern data science [SH05, Zaf09, ZVB + 16, SLLC17,RLH20]. The use of nonnegativity constraint in both NMF and NTF is crucial in obtaining a "parts-based" representation of the input signals [LS99]. Given a d × T data matrix X ∈ R d ×T and an integer parameter r ≥ 1, consider the following constrained matrix factorization problem arg min where the two factors W and H are called dictionary and code matrices of X , respectively, and λ ≥ 0 is a 1 -regularizer for the code matrix H . Depending on the application contexts, we may impose some constraints Θ (1) and Θ (2) on the dictionary and code matrices, respectively, such as nonnegativity or some other convex constraints. An interpretation of this approximate factorization is that the r columns of W give an approximate basis for spanning the n columns of X , where the columns of H give suitable linear coefficients for each approximation. In dictionary learning problems [OF97, EAH99, LS00, EA06, LHK05] , one seeks for a sparse representation of the columns of X with respect to and an over-complete dictionary W , for which one can take r > d and λ > 0.
More generally, we can formulate a similar constrained factorization problem in a tensor setting. Suppose a data tensor X ∈ R I 1 ×···×I m is given and fix an integer R ≥ 1. In the CANDECOMP/PARAFAC (CP) decomposition of X [KB09], we would like to find R loading matrices U (i ) ∈ R I i ×R for i = 1, . . . , m such that the sum of the outer products of their respective columns approximate X: where U (i ) [:, k] denotes the k th column of the I i × R loading matrix matrix U (i ) and denotes the outer product. As an optimization problem, the above CP decomposition model can be formulated as the following the constrained tensor factorization problem: where Θ (i ) ⊆ R I i ×R denotes a compact and convex constraint set and λ i ≥ 0 is a 1 -regularizer for the i th loading matrix U (i ) for i = 1, . . . , m. In particular, by taking λ i = 0 and Θ (i ) to be the set of nonnegative I i × R matrices with bounded norm for i = 1, . . . , m, (24) reduces to the nonnegative CP decomposition (NCPD) [SH05,Zaf09]. Also, It is easy to see that (24) is equivalent to arg min U (1) ∈Θ (1) ,...,U (m) ∈Θ (i ) X − Out(U (1) , . . . ,U (m−1) ) × m (U (m) ) T 2 which is a CP-dictionary-learning problem introduced in [SLN20]. Here × m denotes the mode-m product (see [KB09]) the outer product of loading matrices U (1) , . . . ,U (m) is defined as Namely, we can think of the m-mode tensor X as I m observations of (m − 1)-mode tensors, and the R rank-1 tensors in Out(U (1) , . . . ,U (m) ) serve as dictionary atoms, whereas the tranpose of the last loading matrix U (m) can be regarded as the code matrix (see Figure 1). In particular, assuming m = 2, (25) becomes the constrained matrix factorization problem in (23).
The constrained tensor factorization problem (24) falls under the framework of BCD in (1), where the objective function f CTF is convex in each loading matrix U (i ) for i = 1, . . . , m. Indeed, BCD is a popular approach for both NMF and NTF problems [KHP14]. Namely, when we apply BCD (2) for (24), each block update amounts to solve a quadratic problem under convex constraint. BCD for (24) is known as the form of alternating least squares (ALS). For NMF, ALS (or vanila BCD in (2)) is known to converge to stationary points [GS00]. However, for NTF with m ≥ 3 modes, global convergence to stationary points of ALS is not guaranteed in general [KB09] and requires some additional regularity conditions [Ber97,GS00]. Applying our BCD with diminishing radius, we derive the following ALS with diminishing radius for (24) in Algorithm 2.

3:
Update loading matrices θ n = [U (1) n , · · · ,U (m) n ] by 4: For i = 1, · · · , m do: (unfold(·, i ) denotes the mode-i tensor unfolding (see [KB09])) 5: end for 6: end for 7: output: θ N An immediate corollary of our Theorem 2.1 (i) is that Algorithm 2 always converges to the stationary points of the objective function for the constrained tensor factorization problem (24). Moreover, Theorem 2.1 (ii)-(iii) imply that the worst-case rate of convergence is of O((log n) 2 /n) with worst-case iteration complexity of O(ε −1 (log ε −1 ) 2 ). Furthermore, by Theorem 2.2, these results hold even when the sub-problems of nonnegative least squares are solved approximately as long as the optimality gaps are uniformly bounded. These results, which we summarize in Corollary 5.1, are new especially in the context of NCPD.
Corollary 5.1. Let θ n := [U (1) n , . . . ,U (m) n ], n ≥ 0 denote the output of Algorithm 2 with ∞ n=1 w n = ∞ and n=1 w 2 n < ∞. Then the following hold: (i) θ n converges to the stationary points of the objective function f CTF in (24) for every data tensor X and initial estimate θ 0 .
(ii) If w n = 1/( n log n), then the worst-case convergence rate up to iteration n is of order (log n) 2 /n in the sense of Theorem 2.1 (ii). Furthermore, the worst-case iteration complexity N ε for Algorithm 2 satisfies N ε = O(ε −1 (log ε −1 ) 2 ).
(iii) Suppose θ n for n ≥ 1 are computed approximately from Algorithm 2 such that the optimality gap is uniformly summable. Then (i)-(ii) still hold.
Remark 5.2. In a recent joint work with Strohmeier and Needell [SLN20], we have developed an algorithm for an online version of constrained tensor factorization and convergence analysis under mild assumptions. The analysis we provide in this work plays an important role in the reference.

EXPERIMENTAL VALIDATION
In this section, we compare the performance of our proposed BCD-DR algorithm (Algorithm 1, implemented as Algorithm 2 for NCPD) for the task of NCPD (24) (with no L 1 -regularization, i.e., λ 1 = λ 2 = λ 3 = 0) against two most popular approaches in practice: 1) The vanila BCD (2) (known as Alternating Least Squares for NCPD); and 2) Multiplicative Update (MU) (see [SH05]). Recall that we recover the standard ALS from our Algorithm 2 of ALS with diminishing radius by setting c w n = ∞ for n ≥ 1. We consider one synthetic and two real-world tensor data as follow: (1) X synth ∈ R 300×300×300 ≥0 equals Out(V 1 ,V 2 ,V 3 ), where the true loading matrices V 1 ∈ R 300×50
(2) X Twitter ∈ R 90×5000×1000 ≥0 (days×words×tweets) is the anonymized Twitter text data related to the COVID-19 pandemic from Feb. 1 to May 1 of 2020, which is recently obtained and analyzed in [KKL + 20]. The three modes correspond to days, words, and tweets, in order. In each day, the top 1000 most retweeted English tweets are collected and encoded as a 5000-dimensional tf-idf vector [RU11].
(3) X Headlines ∈ R 203×7000×700 ≥0 (months × words × headlines) is processed in [KKL + 20] from "A Million News Headlines" data, which consists of news headlines published over 203 months years sourced from the Australian news source ABC [Kul18]. The three modes correspond to months, words, and headlines. FIGURE 2. Comparison of the performance of BCD-DR (Algorithm 1) for the nonnegative CPdecomposition (NCPD) problem against BCD and Multiplicative Update (MU). BCD-DR is implmented as Algorithm 2 with c = X F /10 5 (X denoting the data tensor) and weights w n = n −β / log n for n ≥ 1. BCD is implmented as Algorithm 2 with c w n = ∞ for n ≥ 1 (equivalent to Alternating Least Sqaures). For each data tensor, we apply each algorithm ten times from the same random initialization to find nonnegative loading matrices U 1 ,U 2 ,U 3 with R = 5 columns. The average reconstruction error with 1 standard deviation are shown by the solid lines and shaded regions of respective colors.
For each data tensor X of shape I 1 × I 2 × I 3 , we used all three algorithms to learn three loading matrices U i ∈ R I i ×R for i = 1, 2, 3 with R = 5 columns. We plot the objective function in (24) against elapsed time (CPU time) in both cases in Figure 2. Each algorithm is used 10 times for the same data, and the plot shows the average relative reconstruction error (the square-root of f CTF in (24) divided by X F ) together with their standard deviation in shades.
In all experiments, BCD and BCD-DR with various hyperparameters show similar performance, all significantly outperforming MU. Interestingly, for the synthetic data (Figure 2 left), BCD-DR converges faster with stronger radius restriction and β = 5 (in blue) gives the best performance. BCD (in black) converges slower than all BCD-DR variants. Note that for β = 5, the weights w n are summable so the worst-case rate of convergence in Theorem 2.1 (i) is not meaningful. Hence this experimental result indicates that, in some cases, the average speed of convergence of BCD-DR could be significantly faster than in the worst-case. Clearly, if w n decays so fast that there is no stationary point within the 'total search range' ∞ n=1 w n , then no subsequence can converge to a stationary point (i.e., the left hand side of Theorem 2.1 (ii) cannot vanish). From this, we may conjecture that an optimal average rate of convergence of BCD-DR may be achieved when w n 's are summable but decays slow enough to be able to reach stationary points within the total search range. Further investigation on this matter is subject for a future study.