1 Introduction
A great number of timely applications require solving optimization problems over a network where nodes can only communicate with their direct neighbors. This may be due to the need of distributing storage and computation loads (e.g. training large machine learning models
[lian2017can]), or to avoid transferring data that is naturally collected in a decentralized manner, either due to the communication costs or to privacy reasons (e.g. sensor networks [wan2009event], edge computing [alrowaily2018secure]).Specifically, we consider a setting where the nodes want to solve the decentralized optimization problem
(1) 
where each local function is known only by node and nodes can exchange optimization values (parameters, gradients) but not the local functions themselves. We represent the communication network as a graph with nodes (agents) and edges, which are the links used by the nodes to communicate with their neighbors.
Problem (1) was formally introduced in [nedic2007rate] and widely studied ever since. A convenient reformulation often adopted in the literature assigns to each node a local variable and forces consensus between node pairs connected by an edge:
(2a)  
subject to  (2b) 
where indicates that edge links nodes and . Decentralized algorithms to solve (2) allow all nodes to find the minimum value of (1) by just communicating with their neighbors and updating their local variables. This is in contrast with broadcast AllReduce algorithms [rabenseifner2004optimization] or parallel distributed architectures [xiao2019dscovr], which were recently shown to be slower than decentralized schemes in some scenarios [lian2017can].
Here we use reformulation (2) to propose an asynchronous decentralized algorithm where nodes activate at any time uniformly at random, and once activated they choose one of their neighbors to make an update. Methods with such minimal coordination requirements avoid incurring extra costs of synchronization that may also slow down convergence, which is the reason why many algorithms for this asynchronous setting have been proposed in the literature [iutzeler2013asynchronous, wei2013convergence, xu2017convergence, pu2020push, srivastava2011distributed, ram2009asynchronous]
. However, most of these works assume that when a node activates, it simply selects the neighbor to contact randomly, based on a predefined probability distribution. This approach overlooks the possibility of letting nodes
choose the neighbor to contact taking into account the optimization landscape at the time of activation. Therefore, here we depart from the probabilistic choice and ask: can nodes pick the neighbor smartly to make the optimization process converge faster?In this paper we give an affirmative answer and propose an algorithm that achieves this by solving the dual problem of (2). In the dual formulation, there is one dual variable per constraint , hence each dual variable can be associated with an edge in the graph. Our algorithm lets an activated node contact a neighbor so that together they update their shared variable with a gradient step. In particular, we propose to select the neighbor such that the updated is the one whose directional gradient for the dual function is the largest, and thus the one that provides the greatest cost improvement at that iteration. Such optimal choice for asynchronous decentralized optimization has not yet been considered in the literature.
Interestingly, the above protocol where a node activates and selects a to update can be seen as applying the coordinate descent (CD) method [nesterov2012efficiency] to solve the dual problem of (2), with the following key difference: unlike standard CD methods, now only a small subset of coordinates are accessible at each step, which are the coordinates associated with the edges connected to the node activated. Moreover, our proposal of updating the with the largest gradient is similar to the GaussSouthwell (GS) rule[nutini2015coordinate], but applied only to the parameters accessible by the activated node.
We name such protocols setwise CD algorithms and we analyze, in particular, both random uniform sampling and the GS rule for the coordinate selection within the accessible set. To the best of our knowledge, convergence rates for setwise CD schemes have not yet been explored; hence, it is not known what speedup the GS rule can provide compared to uniform sampling in this setting. Furthermore, there are three difficulties that complicate the analysis and constitute the base of our contributions, namely: (i) for arbitrary graphs, the dual problem of (2) has an objective function that is not strongly convex, even if the primal functions are strongly convex, (ii) the fact that the GS rule is applied to a few coordinates only prevents the use of standard norms to obtain the linear rate, as commonly done for CD methods [nesterov2012efficiency, nutini2015coordinate, nutini2017let], and (iii) the fact that the coordinate sets are overlapping (i.e. nondisjoint) makes the problem even harder.
For this reason, we develop a methodology where we prove strong convexity in norms uniquely defined for each algorithm considered. In particular, for the setwise GS rule this requires relating the norm that we originally define to an alternative norm that considers nonoverlapping sets, for which the problem becomes easier and solvable analytically.
Finally, our results also apply to the parallel distributed setting where the parameter vector is stored at a single server and workers can update different subsets of its entries
[tsitsiklis1986distributed, peng2016arock, xiao2019dscovr]. We show an example in our simulations.Our contributions can be summarized as follows:

We introduce the class of setwise CD algorithms and analyze two variants to pick the coordinate to update in the activated set: one that uses uniform sampling (SUCD), and another that applies the GS rule (SGSCD).

We show that this class of algorithms can be used to solve (2) asynchronously, and we provide the linear convergence rates of the two variants considered when the primal functions are smooth and strongly convex.

To obtain these rates for SUCD and SGSCD, we prove strong convexity in uniquelydefined norms that, respectively (i) take into account the graph structure to show strong convexity in the linear subspace where the coordinate updates are applied, and (ii) account for both the random uniform node activation and the application of the GS rule to just a subset of the coordinates.

We show that the speedup of SGSCD with respect to SUCD can be up to (the size of the largest coordinate set), which is analogous to the that of the GS rule with respect to random uniform coordinate sampling in centralized CD [nutini2015coordinate].
2 Related work
A number of algorithms have been proposed to solve (1) in the asynchronous setup that we consider here. In [ram2009asynchronous], the activated node chooses a neighbor uniformly at random and both nodes average their primal local values. In [iutzeler2013asynchronous] the authors adapted the ADMM algorithm to the decentralized setting, but it was the ADMM of [wei2013convergence] the first one shown to converge at the same rate as the centralized ADMM. The algorithm of [xu2017convergence] tracks the average gradients to converge to the exact optimum instead of just a neighborhood around it, as many algorithms back then. The algorithm of [pu2020push] can be used on top of directed graphs, which impose additional challenges. A key novelty of our scheme, compared to this line of work, is that we consider the possibility of letting the nodes choose the neighbor to contact in order to make convergence faster.
Work [verma2021max] is, to the best of our knowledge, the only work similarly considering smart neighbor selection. The authors propose MaxGossip, a version of [nedic2007rate] where the activated node averages its local (primal) parameter with that of the neighbor with whom the parameter difference in the largest. They consider convex scalar functions , and use Lyapunov analysis to prove convergence to an optimal value. In contrast, here we obtain linear convergence rates for smooth and strongly convex using duality theory.
Moreover, our rate results for SUCD and SGSCD extend the results in [nutini2015coordinate], where the GS rule was shown to be up to times faster than uniform sampling for , to the case where this choice is constrained to a subset of the coordinates only, sets have different sizes, each coordinate belongs to exactly two sets, and sets activate uniformly at random. This matches not only the decentralized case, but also parallel distributed settings such as [tsitsiklis1986distributed, peng2016arock, xiao2019dscovr].
3 Dual formulation
In this section, we define notation that will be used in the rest of the paper, obtain the dual problem of (2), and analyze the properties of the dual objective function. We will assume throughout that the functions are smooth and strongly convex:
We define the concatenated primal and dual variables and , respectively. The graph’s incidence matrix has exactly one 1 and one 1 per column , in the rows corresponding to nodes , and zeros elsewhere (the choice of sign for each node is irrelevant). We call the vector that has 1 in entry and 0 elsewhere; we define in the same way, with the only difference being the dimension. Vectors and are respectively the allone and allzero vectors, and is the identity matrix. Finally, we define the block arrays and , where is the Kronecker product.
where (a) holds due to Lagrange duality and (b) holds by strong duality (see e.g. Sec. 5.4 in [boyd2004convex]). Functions are the Fenchel conjugates of the , and are defined as
Our setwise CD algorithms converge to the optimal solution of (2) by solving (3). In particular, they update a single dual variable at each iteration and converge to some minimum value of . Since each is associated with an edge of the network, the setwise CD algorithms can run asynchronously.
We now state the convexity properties of . Since the objective in (2a) is smooth and strongly convex in , with and , function is smooth with , where
is the largest eigenvalue of
(Sec. 4 in [uribe2020dual]). We also define as the smallest nonzero eigenvalue^{1}^{1}1The “+” stresses that is the smallest strictly positive eigenvalue. of .However, as shown next, function is not strongly convex in the standard L2 norm, which is a property that facilitates the performance analysis of many linear rate optimization methods in the literature.
Proposition 1.
is not strongly convex in .
Proof.
Since does not have full column rank in the general case (i.e., unless the graph is a tree), there exist such that and . ∎
Nevertheless, we can still show linear rates for the setwise CD algorithms using the fact that is strongly convex in a linear subspace of , as stated next.
Proposition 2 (Appendix C of [hendrikx2019accelerated]).
is strongly convex in the seminorm , with .
In the definition of the seminorm , denotes the pseudoinverse of . A key fact for the proofs in the next section is that matrix is a projector onto , the column space of .
In order to make the definitions and notation simpler, in the next section we assume that , so that , , and the gradient of in the direction of is a scalar. After our theoretical analysis and presentation of the results, in Sec. 4.3 we discuss the modifications needed to adapt them to the case .
4 Setwise Coordinate Descent Algorithms
In this section we present the setwise CD algorithms, which can solve generic convex problems such as (3) optimally and asynchronously. We propose two setwise CD algorithms: (i) one where the coordinate to update is selected uniformly at random within the accessible set of coordinates (SUCD), and (ii) one where we pick the coordinate applying the GS rule to the coordinates in the available set (SGSCD).
If coordinate is updated at iteration , under the simplification the generic CD update applied to is:
(4) 
where is the stepsize. Since is smooth, choosing guarantees descent at each iteration [nutini2015coordinate]:
(5) 
Eq. (5) will be the departure point to prove the linear convergence rates of SUCD and SGSCD.
We now define formally the setwise CD algorithms.
Definition 1 (Setwise CD algorithm).
In a setwise CD algorithm, every coordinate is assigned to (potentially multiple) sets , such that all coordinates belong to at least one set. At any point in time, a set might activate with uniform probability among the . When a activates, the setwise CD algorithm chooses a single coordinate to update using (4).
The next remark shows how the decentralized problem (2) can be solved asynchronously with setwise CD algorithms.
Remark 1.
In light of Remark 1, in the following we illustrate the steps that should be performed by the nodes to run the setCD algorithms to find a . We first note that the gradient of in the direction of for is
(6) 
Nodes can use (4) and (6) to update the variables that they have access to (i.e., those corresponding to the edges they are connected to) as follows: each node keeps in memory the current values of , which are needed to compute . Then, when edge needs to be updated (either because node activated and contacted , or vice versa), both and compute their respective terms in the right hand side of (6) and exchange them through their link. Finally, both nodes compute (6) and update their local copy of applying (4).
Algorithms 1 and 2 below detail these steps for SUCD and SGSCD, respectively. In the algorithms we have used to indicate the set of neighbors of node (note that ). Table 1 shows this and other setrelated notation that will be frequently used in the sections that follow.
We now proceed to describe the SUCD and SGSCD algorithms in detail, and prove their linear convergence rates.
4.1 Setwise Uniform CD (SUCD)
In SUCD, the activated node chooses the neighbor uniformly at random, as shown in Alg. 1. We can compute the periteration progress of SUCD taking expectation in (5):
(7) 
where and .
The standard procedure to show the linear convergence of CD in the centralized case is to lowerbound using the strong convexity of the function [nesterov2012efficiency, nutini2015coordinate]. However, since is not strongly convex (Prop. 1), we cannot apply this procedure to get the linear rate of SUCD.
We can, however, use the strong convexity of in instead (Prop. 2). The next result gives the core of the proof.
Proposition 3.
It holds that
where is the dual norm of , defined as (see e.g. Sec. A.1.6 in [boyd2004convex])
(8) 
Proof.
Note that such that , it holds that and thus . This means that , and therefore it holds that . Finally, since the dual norm of the L2 norm is again the L2 norm, we have that also , which gives the result. ∎
We now use Prop. 3 to prove the linear rate of SUCD.
Proposition 4 (Rate of SUCD).
SUCD converges as
Proof.
Since is strongly convex in with strong convexity constant (Prop. 2), it holds
Minimizing both sides of the above equation respect to as in Sec. 4 in [nutini2015coordinate] we get
(9) 
and rearranging terms we can lowerbound .
Note that vector has coordinates, where the inequality holds with equality for regular graphs. We make the following remark.
Remark 2.
If is regular, the linear convergence rate of SUCD is , which matches the rate of centralized uniform CD for strongly convex functions [nesterov2012efficiency, nutini2015coordinate], with the only difference that now the strong convexity constant is defined over norm .
In the next section we analyze SGSCD and show that its convergence rate can be up to times that of SUCD.
4.2 Setwise GaussSouthwell CD (SGSCD)
In SGSCD, as shown in Alg. 2, the activated node selects the neighbor to contact applying the GS rule within the edges in :
and then satisfies . In order to make this choice, all nodes must send their to node (line 5 in Alg. 2). We discuss this additional communication step of SGSCD with respect to SUCD in Sec. 6.
To obtain the convergence rate of SGSCD we will follow the steps taken for SUCD in the proof of Prop. 4. As done for SUCD, we start by computing the periteration progress taking expectation in (5) for SGSCD:
(10) 
Given this periteration progress, to proceed as we did for SUCD we need to show (i) that the sum on the right hand side of (10) defines a norm, and (ii) that strong convexity holds in its dual norm. We start by defining the selector matrices , which will significantly simplify notation.
Definition 2 (Selector matrices).
The selector matrices select the coordinates of a vector in that belong to set . Note that any vertical stack of the unitary vectors gives a valid .
We can now show that the sum in (10) is a (squared) norm. Since the operation involves applying within each set , we will denote this norm , where the subscript SM stands for “SetMax”.
Proposition 5.
The function is a norm in .
Proof.
Using and we can show that satisfies the triangle inequality. It is straightforward to show that and iff . ∎
Following the proof of Prop. 4, we would like to show that is strongly convex in the dual norm . Furthermore, we would like to compare the strong convexity constant with to quantify the speedup of SGSCD with respect to SUCD. It turns out, though, that computing is not easy at all; the main difficulty stems from the fact that sets are overlapping (or nondisjoint), since each coordinate belongs to both and . The first scheme in Figure 1 illustrates this fact for the 3node clique.
To circumvent this issue, we define a new norm (“SetMax NonOverlapping”) that we can directly relate to (Prop. 6) and whose value we can compute explicitly (Prop. 7), which will later allow us to relate its strong convexity constant to (Prop. 8).
Definition 3 (Norm ).
We assume that each coordinate is assigned to only one of the sets or , such that the new sets are nonoverlapping (some sets can be empty), and all coordinates belong to exactly one set in . We name the selector matrices of these new sets , so that each possible choice of defines a different set . Then, we define
(11) 
with the choice of nonoverlapping sets
(12) 
The definition of sets corresponds to assigning each edge to one of the two nodes at its endpoints, as illustrated in the second scheme of Figure 1. Therefore, for each possible pair we can define a complementary pair such that if was assigned to in , then it is assigned to in . This corresponds to assigning to the opposite endpoint (node) to the one originally chosen, as shown in the third scheme of Figure 1. With these definitions, it holds (potentially with some permutation of the rows):
We remark that the equality above holds for any corresponding to a feasible assignment , and in particular it hols for . This fact is used in the proof of the following proposition, which relates norms and . This will allow us to complete the analysis with , which we can compute explicitly (Prop. 7).
Proposition 6.
The value of the dual norm of , denoted , satisfies .
Proof.
By definition
By inspection we can tell that the that attains the supremum, denoted , will satisfy . We note now that
(13) 
with
(14) 
The next proposition gives the value of explicitly, which will be needed to compare the strong convexity constant with .
Proposition 7.
It holds that .
Proof.
Since the sets are nonoverlapping and in (11) norm is applied perset, the entries of will have and the sign will match that of the entries of , i.e. . The maximization of (11) then becomes
subject to 
Factoring out in the objective and noting that , we can define and so that (11) now reads
The right hand side is the definition of the dual of the L2 norm evaluated at . Since the dual of the L2 norm is again the L2, we have . ∎
We can now prove the linear convergence rate of SGSCD.
Proposition 8 (Rate of SGSCD).
SGSCD converges as
with
(15) 
Proof.
We start by proving (15) by showing that strong convexity in implies strong convexity in , which will give the inequalities as a byproduct of the analysis. Below we assume that ; the results here can then be directly applied to the proofs above because and their duals are applied to , which is always in (Prop 3).
We also note that, using the CauchySchwarz inequality and denoting the ^{th} entry of vector , it holds both that
where . We can summarize these relations as
Using these inequalities in the strong convexity definitions, similarly to [nutini2015coordinate], we get both
(16)  
and
(17)  
Equation (16) says that is at least strongly convex in , and eq. (17) says that is at least strongly convex in . Together they imply (15).
To get the rate of SGSCD, and following the procedure of SUCD, we need to lowerbound the periteration progress in (10). For this we will use the strong convexity in , which we can obtain from the strong convexity that we just proved for , as shown next.
Stating that is at least strongly convex in and using Prop. 6 we obtain:
(18)  
from where we conclude that .
Minimizing both sides of the first inequality in (18) respect to we obtain
(19) 
which is analogous to (9), and rearranging terms gives a lower bound on . Using this lower bound in (10) and replacing gives the rate of SGSCD.
∎
Proposition 8 states that SGSCD can be up to faster than SUCD. This result is analogous to that of [nutini2015coordinate] for the GS rule respect to uniform sampling in centralized CD.
Although this is an upper bound and may not always be achievable, we can think of the following scenario where this gain is attained: let all sets have the same size , exactly out of the coordinates in each set have , and only one have . In this case, on average only times will SUCD choose the coordinate that gives some improvement, while SGSCD will do it at all iterations.
Note that this example requires the gradients of all coordinates to be independent, which is not verified in the decentralized optimization setting: according to eq. (6), for a to be zero, it must hold that . But unless this equality holds for all (i.e., unless the minimum has been attained), will continue changing, and the will differ. Thus, the gains of SGSCD in this setting may not attain the upper bound.
Nevertheless, when it comes to parallel distributed setups, the coordinates are not necessarily coupled as in the decentralized case, and thus the speedup of SGSCD is still achievable, as shown in our simulations below.
4.3 Case
To extend the proofs above for , the block arrays and should be used instead of and , and the selector matrices should be redefined in the same way. Then, all the operations that in the proofs above are applied per entry (scalar coordinate) of the vector , should now be applied to the magnitude of each vector coordinate of . Also, since , in this case the GS rule becomes .
5 Numerical Results
, the thick transparent lines show the part of the curves used to estimate
and for SUCD and SGSCD, respectively. The ratio increases notably with , in agreement with the theory.Figure 2 shows the remarkable speedup of SGSCD with respect to SUCD in both the decentralized (left plots) and the parallel distributed (right plots) settings.
For the decentralized setting we created two regular graphs of nodes and degrees and 12, respectively. The local functions were with , and if modulo and otherwise, where is the index of each node. We chose these so that each node would have (approximately) one neighbor out of the with whom the coordinate gradient would have maximum disagreement, thus maximizing the chances of observing differences between SUCD and SGSCD.
For the parallel distributed setting, we created a problem that was separable percoordinate, and we tried to recreate the conditions described in the previous section to approximate the gain. We chose with and . Matrix was diagonal with its nonzero entries sampled from . We then created sets of coordinates such that each coordinate belonged to exactly two sets, similarly to the parallel distributed scenario with parameter server where each worker has access to a subset of the coordinates only. We simulated two different distributions of the coordinates: one with sets of coordinates each, and another with sets of coordinates each. Following the reasoning in the previous section, we set the initial value of coordinates in each set to (close to the optimal value ), and the one remaining to (far away from ).
The plots in Figure 2 show the steep rate gain of SGSCD with respect to SUCD as