On the convergence rate of the scaled proximal decomposition on the graph of a maximal monotone operator (SPDG) algorithm
Abstract
Relying on fixed point techniques, Mahey, Oualibouch and Tao introduced in [11] the scaled proximal decomposition on the graph of a maximal monotone operator (SPDG) algorithm and analyzed its performance on inclusions for strongly monotone and Lipschitz continuous operators. The SPDG algorithm generalizes the Spingarn’s partial inverse method by allowing scaling factors, a key strategy to speed up the convergence of numerical algorithms.
In this note, we show that the SPDG algorithm can alternatively be analyzed by means of the original Spingarn’s partial inverse framework, tracing back to the 1983 Spingarn’s paper.
We simply show that under the assumptions considered in [11], the Spingarn’s partial inverse
of the underlying maximal monotone operator is strongly monotone, which allows
one to employ recent results on the convergence and iterationcomplexity of proximal point type methods for strongly monotone operators. By doing this, we additionally obtain a potentially faster convergence for the SPDG algorithm and a more accurate upper bound on the number of iterations needed to achieve prescribed tolerances, specially on illconditioned problems.
2000 Mathematics Subject Classification: 90C25, 90C30, 47H05.
Key words: SPDG algorithm, partial inverse method, strongly monotone operator, rate of convergence, complexity.
Introduction
In his 1983 paper, J. E. Spingarn [22] posed the problem of finding such that
(1) 
where is a (pointtoset) maximal monotone operator on the Hilbert space and is a closed vector subspace. Problem (1) encompasses different problems in applied mathematics and optimization including minimization of convex functions over subspaces and decomposition problems for the sum of finitely many maximal monotone operators (see, e.g., [1, 5, 16, 22]). Spingarn also proposed and studied in [22] a proximal point type method – the Spingarn’s partial inverse method – for solving (1). This is an iterative scheme, which consists essentially of the Rockafellar’s proximal point (PP) method [18] for solving the inclusion problem defined by the partial inverse of the underlying maximal monotone operator. Since then, the Spingarn’s partial inverse method has been used by many authors as a framework for the design and analysis of different practical algorithms in optimization and related applications.
One of the problems that arises in Spingarn’s approach is the difficulty of dealing with scaling, a key strategy to speed up the convergence rate of numerical algorithms. Relying on fixed point techniques, Mahey, Oualibouch and Tao [11] circumvented this problem by proposing and analyzing the scaled proximal decomposition on the graph of a maximal monotone operator (SPDG) algorithm for solving (1). In contrast to the Spingarn’s partial inverse method, the SPDG algorithm allows for a scaling of the problem, for which the impact on speeding up its convergence rate was observed in [11] on illconditioned quadratic programming problems. The analysis of the SPDG algorithm presented in [11] consists of reformulating it as a fixed point iteration for a certain fixed point operator. This differs from the original Spingarn’s approach in [22], which is supported on the partial inverse of monotone operators, a concept introduced and coined by Spingarn himself. Moreover, one of the main contributions of Mahey, Oualibouch and Tao is the convergence rate analysis of the SPDG algorithm under strong monotonicity. In [11, Theorem 4.2], they proved that if the maximal monotone operator in (1) is strongly maximal monotone and Lipschitz continuous, then the SPDG algorithm converges at a linear rate to the (unique, in this case) solution of (1).
In this note, we show that the SPDG algorithm can alternatively be analyzed within the Spingarn’s partial inverse framework, tracing back to the original Spingarn’s approach. We simply show that under the assumptions considered in [11], the Spingarn’s partial inverse of the underlying maximal monotone operator is strongly monotone, which allows one to employ recent results on the convergence and iterationcomplexity of proximal point type methods for strongly monotone operators. By doing this, we additionally obtain a potentially faster convergence for the SPDG algorithm and a more accurate upper bound on the number of iterations needed to achieve prescribed tolerances, specially on illconditioned problems.
This work is organized as follows. Section 1 presents the basic notation to be used in this note and some preliminaries results. In Section 2, we present our main contributions, namely the convergence rate analysis of the SPDG algorithm within the Spingarn’s partial inverse framework. Section 3 is devoted to some concluding remarks.
1 Notation and basic results
Let be a real Hilbert space with inner product and induced norm . A setvalued map is said to be a monotone operator if for all and . On the other hand, is maximal monotone if is monotone and its graph is not properly contained in the graph of any other monotone operator on . The inverse of is , defined at any by if and only if . The resolvent of a maximal monotone operator is and if and only if . The operator , where , is defined by . A maximal monotone operator is said to be strongly monotone if and for all and .
The Spingarn’s partial inverse [22] of a maximal monotone operator with respect to a closed subspace of is the (maximal monotone) operator whose graph is
(2) 
where and stand for the orthogonal projection onto and , respectively.
In the next subsection, we present the convergence rate of the proximal point algorithm for finding zeroes of strongly monotone inclusions.
1.1 On the proximal point method for strongly monotone operators
In this subsection, we consider the problem
(3) 
where is a strongly maximal monotone operator, for some , i.e., is maximal monotone and there exists such that
(4) 
The proximal point (PP) method is an iterative scheme for finding approximate solutions of (3) (under the assumption of maximal monotonicity of ). The method traces back to the work of Martinet and was popularized by Rockafellar, who formulated and studied some of its inexact variants along with important applications in convex programming [17, 18].
Among the many inexact variants of the PP method, the hybrid proximal extragradient (HPE) method of Solodov and Svaiter [19] has been the object of intense study by many authors (see, e.g., [3, 4, 6, 7, 8, 9, 10, 12, 13, 15, 19, 20, 21]). One of its distinctive features is that it allows for inexact solution of the corresponding subproblems within relative errors tolerances. Contrary to the (asymptotic) convergence analysis of the HPE method, which was originally established in [19], its iterationcomplexity was studied only recently by Monteiro and Svaiter in [14]. Motivated by the main results on pointwise and ergodic iterationcomplexity which were obtained in the latter reference, nonasymptotic convergence rates of the HPE method for solving strongly monotone inclusions were analyzed in [3].
In this subsection, we specialize the main result in latter reference, regarding the iterationcomplexity of the HPE method for solving strongly monotone inclusions, to the exact PP method for solving (3). This is motivated by the fact that under certain conditions on the maximal monotone operator , its partial inverse – with respect to a closed subspace – is strongly maximal monotone (see Proposition 2.2).
Next, we present the version of the PP method that we need in this work.
Algorithm 1.
Proximal point (PP) method for solving (3)The PP method consists in successively applying the resolvent of the operator (which is well defined due to the Minty theorem). As we mentioned earlier, there are more general versions of Algorithm 1 in which, in particular, the stepsize parameter is allowed to vary along the iterations and is computed only inexactly in (5).
The following result establishes the linear convergence of the PP method under strong monotonicity assumption on the operator . Although it is a direct consequence of a more general result (see [3, Proposition 2.2]), here we present a short proof for the convenience of the reader.
Proposition 1.1.
Proof.
The desired results follow from the following
where we have used that , from (5), , is strongly monotone and . ∎
As we mentioned earlier, in the next section we analyze the global (nonasymptotic) convergence rate of the SPDG algorithm under the assumptions that the operator in (1) is strongly monotone and Lipschitz continuous [11]. We will prove, in particular, that under such conditions on , the partial inverse is strongly monotone as well, in which case the results in (6) and (7) can be applied.
2 On the convergence rate of the SPDG algorithm
In this section, we consider problem (1), i.e., the problem of finding such that
(8) 
where the following hold:

is a closed vector subspace of .

is (maximal) strongly monotone, i.e., is maximal monotone and there exists such that
(9) 
is Lipschitz continuous, i.e., there exists such that
(10)
The convergence rate of the SPDG algorithm under the assumptions A2) and A3) was previously analyzed in [11, Theorem 4.2]. Note that (9) and (10) imply, in particular, that the operator is at most singlevalued and . The number is known in the literature as the condition number of the problem (8). The influence of as well as of scaling factors in the convergence speed of the SPDG algorithm, specially for solving illconditioned problems, was discussed in [11, Section 4] for the special case of quadratic programming.
In this section, analogously to the latter reference, we analyze the convergence rate of the SPDG algorithm (Algorithm 2) under assumptions A2) and A3) on the maximal monotone operator . We show that the SPDG algorithm falls in the framework of the PP method (Algorithm 1) for the (scaled) partial inverse of , which, under assumptions A2) and A3), is shown to be strongly monotone. This contrasts to the approach adopted in [11], which relies on fixed point techniques. By showing that the (scaled) partial inverse of – with respect to – is strongly monotone, we obtain a potentially faster convergence for the SPDG algorithm, when compared to the one proved in [11] by means of fixed point techniques. Moreover, the convergence rates obtained in this note allows one to measure the convergence speed of the SPDG algorithm on three different measures of approximate solution to the problem (8) (see Theorem 2.4 and the remarks right below it).
Among the above mentioned measures of approximate solution, one of them allows for the study of the iterationcomplexity of the SPDG algorithm in the lines of recent results on the iterationcomplexity of the inexact Spingarn’s partial inverse method [2] (see (33)). In this regard, one can compute SPDG algorithm’s iterationcomplexity with respect to the following notion of approximate solution of (8) (see [2]): for a given tolerance , find such that
(11) 
where . For , criterion (11) gives , and , i.e., in this case the pair is a solution of (8). We mention that criterion (11) naturally appears in different settings and has not been considered in [11].
Next, we present the scaled proximal decomposition on the graph of a maximal monotone operator (SPDG) algorithm.
Let , , be given and set . Compute (12) If and , then stop. Otherwise, define (13) set and go to step 1.
Remarks.

Algorithm 2 was originally proposed and studied in [11]. When , it reduces to the Spingarn’s partial inverse method for solving (8). The authors of the latter reference emphasize the importance of introducing the scaling in order to speed up the convergence of the SPDG algorithm, specially when solving illconditioned problems.

As we mentioned earlier, one of the contributions of this note is to show that similar results (actually potentially better) to the one obtained in [11] regarding the convergence rate of Algorithm 2 can be proved by means of the Spingarn’s partial inverse framework, instead of fixed point techniques.
The following result appears (with a different notation) inside the proof of [11, Theorem 4.2].
Theorem 2.1.
Remarks.

It follows from (16) that, for a given tolerance , Algorithm 2 finds such that after performing at most
(17) iterations. In the third remark after Theorem 2.4, we show that our approach provides a potentially better upper bound on the number of iterations needed by the SPDG algorithm to achieve prescribed tolerances, specially on illconditioned problems.
A direct consequence of the next proposition, in contrast to the reference [11], is that it is possible to analyze the SPDG algorithm within the original Spingarn’s partial inverse framework. We show that under assumptions A2) and A3), the partial inverse operator is strongly monotone.
Proposition 2.2.
Under the assumptions A2) and A3) on the maximal monotone operator , its partial inverse with respect to is strongly (maximal) monotone with
(18) 
Proof.
Take , and note that, from (2), we have
(19) 
which, in turn, combined with the assumption A2) and after some direct calculations yields
(20)  
(21) 
On the other hand, assumption A3) and (19) imply
which, in particular, gives
(22) 
Using (21) and combining (20) and (22) we find, respectively,
The desired result now follows by adding the above inequalities and by using the definition of in (18). ∎
Proposition 2.3.
Proof.
Using (12), we obtain and, as a consequence, from (2), we have
(26) 
From the second identity in (12), we have , which combined with (13) gives
which, in turn, is equivalent to (24). Using (26), (24), (23) and (13), we find , which is clearly equivalent to (25). The last statement of the proposition follows directly from (25) and Algorithm 1’s definition. ∎
In the next theorem, we present the main contribution of this note, namely, convergence rates for the SPDG algorithm obtained within the original Spingarn’s partial inverse framework.
Theorem 2.4.
Proof.
First, note that, from (2), is a solution of (8) if and only if . Using the last statement in Proposition 2.3, Proposition 2.2 to the operator (which is strongly monotone and Lipschitz continuous) and Proposition 1.1, we conclude that the inequalities (6) and (7) hold with as above, , as in (23) and
Direct calculations yield (recall that )
Hence, (27) and (29) follow from (6) and (7), respectively. To finish the proof, it remains to prove (28). To this end, note that it follows from (23), (24), (27) and the facts that and . ∎
Remarks.

Analogously to first remark after Theorem 2.1, one can easily verify that provides the best convergence speed in (27)–(29), in which case we find, respectively,
(30) (31) (32) 
Since , it follows that the convergence speed obtained in (30)–(32) is potentially better (specially on illconditioned problems) than the optimal one obtained for the SPDG algorithm in [11] via fixed point techniques, namely (16). The same remark applies to the rates in (27)–(29), when compared to the corresponding one in (14). See Figure 1.
3 Concluding remarks
In this note, we proved that the SPDG algorithm of Mahey, Oualibouch and Tao introduced in [11], can alternatively be analyzed within the Spingarn’s partial inverse framework, instead of the fixed point approach proposed in the latter reference. This traces back to the 1983 Spingarn’s paper, where, among other contributions, he introduced and analyzed the partial inverse method. We simply proved that under the assumptions of [11], namely strong monotonicity and Lipschitz continuity, the Spingarn’s partial inverse of the underlying maximal monotone operator is strongly monotone as well. This allowed us to employ recent developments in the convergence analysis and iterationcomplexity of proximal point type methods for strongly monotone operators. By doing this, we additionally obtained a potentially better convergence speed for the SPDG algorithm as well as a better upper bound on the number of iterations needed to achieve prescribed tolerances.
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