# Submodular Hamming Metrics

We show that there is a largely unexplored class of functions (positive polymatroids) that can define proper discrete metrics over pairs of binary vectors and that are fairly tractable to optimize over. By exploiting submodularity, we are able to give hardness results and approximation algorithms for optimizing over such metrics. Additionally, we demonstrate empirically the effectiveness of these metrics and associated algorithms on both a metric minimization task (a form of clustering) and also a metric maximization task (generating diverse k-best lists).

## Authors

• 4 publications
• 24 publications
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• 9 publications
• 14 publications
• ### Eliciting Binary Performance Metrics

Given a binary prediction problem, which performance metric should the c...
06/05/2018 ∙ by Gaurush Hiranandani, et al. ∙ 0

• ### A Unified Framework of Robust Submodular Optimization

In this paper, we shall study a unified framework of robust submodular o...
06/14/2019 ∙ by Rishabh Iyer, et al. ∙ 0

• ### Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Submodular maximization has become established as the method of choice f...
10/14/2020 ∙ by Marwa El Halabi, et al. ∙ 8

• ### Metrics which turn tilings into binary perfect codes

In this work, we consider tilings of the Hamming cube and look for metri...
03/14/2019 ∙ by Gabriella Akemi Miyamoto, et al. ∙ 0

• ### Massively Parallel Algorithms and Hardness for Single-Linkage Clustering Under ℓ_p-Distances

We present massively parallel (MPC) algorithms and hardness of approxima...
10/04/2017 ∙ by Grigory Yaroslavtsev, et al. ∙ 0

• ### Negative type diversities, a multi-dimensional analogue of negative type metrics

Diversities are a generalization of metric spaces in which a non-negativ...
09/18/2018 ∙ by Pei Wu, et al. ∙ 0

• ### Learning to Evaluate Perception Models Using Planner-Centric Metrics

Variants of accuracy and precision are the gold-standard by which the co...
04/19/2020 ∙ by Jonah Philion, et al. ∙ 2

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## 1 Introduction

A good distance metric is often the key to an effective machine learning algorithm. For instance, when clustering, the distance metric largely defines which points end up in which clusters. Similarly, in large-margin learning, the distance between different labelings can contribute as much to the definition of the margin as the objective function itself. Likewise, when constructing diverse

-best lists, the measure of diversity is key to ensuring meaningful differences between list elements.

We consider distance metrics over binary vectors, . If we define the set , then each can seen as the characteristic vector of a set , where if , and otherwise. For sets , with representing the symmetric difference, , the Hamming distance is then:

 dH(A,B)=|A△B|=n∑i=11A△B(i)=n∑i=11(1A(i)≠1B(i)). (1)

A Hamming distance between two vectors assumes that each entry difference contributes value one. Weighted Hamming distance generalizes this slightly, allowing each entry a unique weight. Mahalanobis distance generalizes further, allowing weighted pairwise interactions of the following form:

 dM(A,B)=1⊤A△BS1A△B=n∑i=1n∑j=1Sij1A△B(i)1A△B(j). (2)

When is a positive semi-definite matrix, this type of distance is a metric. For many practical applications, however, it is desirable to have entries interact with each other in more complex and higher-order ways than Hamming or Mahalanobis allow. Yet, arbitrary interactions would result in non-metric functions whose optimization would be intractable. In this work, therefore, we consider an alternative class of functions that goes beyond pairwise interactions, yet is computationally feasible, is natural for many applications, and preserves metricity.

Given a set function , we can define a distortion between two binary vectors as follows: . By asking to satisfy certain properties, we will arrive at a class of discrete metrics that is feasible to optimize and preserves metricity. We say that is positive if whenever ; is normalized if ; is monotone if for all ; is subadditive if for all ; is modular if for all ; and is submodular if for all . If we assume that is positive, normalized, monotone, and subadditive then is a metric (see Theorem 3.1), but without useful computational properties. If is positive, normalized, monotone, and modular, then we recover the weighted Hamming distance. In this paper, we assume that is positive, normalized, monotone, and submodular (and hence also subadditive). These conditions are sufficient to ensure the metricity of , but allow for a significant generalization over the weighted Hamming distance. Also, thanks to the properties of submodularity, this class yields efficient optimization algorithms with guarantees for practical machine learning problems. In what follows, we will refer to normalized monotone submodular functions as polymatroid functions; all of our results will be concerned with positive polymatroids. We note here that despite the restrictions described above, the polymatroid class is in fact quite broad; it contains a number of natural choices of diversity and coverage functions, such as set cover, facility location, saturated coverage, and concave-over-modular functions.

Given a positive polymatroid function , we refer to as a submodular Hamming (SH) distance. We study two optimization problems involving these metrics (each is a positive polymatroid, each , and denotes a combinatorial constraint):

 SH-min: minA∈Cm∑i=1fi(A△Bi),andSH-max: maxA∈Cm∑i=1fi(A△Bi). (3)

We will use as shorthand for the sequence , for the sequence , and for the objective function . We will also make a distinction between the homogeneous case where all are the same function, and the more general heterogeneous case where each may be distinct. In terms of constraints, in this paper’s theory we consider only the unconstrained () and the cardinality-constrained (e.g., , ) settings. In general though, could express more complex concepts such as knapsack constraints, or that solutions must be an independent set of a matroid, or a cut (or spanning tree, path, or matching) in a graph.

Intuitively, the SH-min problem can be thought of as a centroid-finding problem; the minimizing should be as similar to the ’s as possible, since a penalty of is paid for each difference. Analogously, the SH-max problem can be thought of as a diversification problem; the maximizing should be as distinct from all ’s as possible, as is awarded for each difference. Given modular (the weighted Hamming distance case), these optimization problems can be solved exactly and efficiently for many constraint types. For the more general case of submodular , we establish several hardness results and offer new approximation algorithms, as summarized in Tables 1 and 2. Our main contribution is to provide (to our knowledge), the first systematic study of the properties of submodular Hamming (SH) metrics, by showing metricity, describing potential machine learning applications, and providing optimization algorithms for SH-min and SH-max.

The outline of this paper is as follows. In Section 2, we offer further motivation by describing several applications of SH-min and SH-max to machine learning. In Section 3, we prove that for a positive polymatroid function , the distance is a metric. Then, in Sections 4 and 5 we give hardness results and approximation algorithms, and in Section 6 we demonstrate the practical advantage that submodular metrics have over modular metrics for several real-world applications.

## 2 Applications

We motivate SH-min and SH-max by showing how they occur naturally in several applications.

Clustering: Many clustering algorithms, including for example -means [1], use distance functions in their optimization. If each item to be clustered is represented by a binary feature vector , then counting the disagreements between and is one natural distance function. Defining sets , this count is equivalent to the Hamming distance . Consider a document clustering application where is the set of all features (e.g., -grams) and is the set of features for document . Hamming distance has value both when

synapse''

} and when . Intuitively, however, a smaller distance seems warranted in the latter case since the difference is only in one rather than two distinct concepts. The submodular Hamming distances we propose in this work can easily capture this type of behavior. Given feature clusters , one can define a submodular function as:

 f(Y)=∑W∈W√|Y∩W|. (4)

Applying this with , if the documents’ differences are confined to one cluster, the distance is smaller than if the differences occur across several word clusters. In the case discussed above, the distances are and . If this submodular Hamming distance is used for -means clustering, then the mean-finding step becomes an instance of the SH-min problem. That is, if cluster contains documents , then its mean takes exactly the following SH-min form:

 μj∈argminA⊆V∑i∈Cjf(A△Bi). (5)

Structured prediction

: Structured support vector machines (SVMs) typically rely on Hamming distance to compare candidate structures to the true one. The margin required between the correct structure score and a candidate score is then proportional to their Hamming distance. Consider the problem of segmenting an image into foreground and background. Let

be image ’s true set of foreground pixels. Then Hamming distance between and a candidate segmentation with foreground pixels counts the number of mis-labeled pixels. However, both [2] and [3] observe poor performance with Hamming distance and recent work by [4] shows improved performance with richer distances that are supermodular functions of . One potential direction for further enriching image segmentation distance functions is thus to consider non-modular functions from within our submodular Hamming metrics class. These functions have the ability to correct for the over-penalization that the current distance functions may suffer from when the same kind of difference happens repeatedly. For instance, if differs from

only in the pixels local to a particular block of the image, then current distance functions could be seen as over-estimating the difference. Using a submodular Hamming function, the “loss-augmented inference” step in SVM optimization becomes an SH-max problem. More concretely, if the segmentation model is defined by a submodular graph cut

, then we have: . (Note that .) In fact, [5] observes superior results with this type of loss-augmented inference using a special case of a submodular Hamming metric for the task of multi-label image classification.

Diverse -best: For some machine learning tasks, rather than finding a model’s single highest-scoring prediction, it is helpful to find a diverse set of high-quality predictions. For instance, [6] showed that for image segmentation and pose tracking a diverse set of solutions tended to contain a better predictor than the top highest-scoring solutions. Additionally, finding diverse solutions can be beneficial for accommodating user interaction. For example, consider the task of selecting photos to summarize the photos that a person took while on vacation. If the model’s best prediction (a set of

images) is rejected by the user, then the system should probably present a substantially different prediction on its second try. Submodular functions are a natural model for several summarization problems

[7, 8]. Thus, given a submodular summarization model , and a set of existing diverse summaries , one could find a th summary to present to the user by solving:

 Ak=argmaxA⊆V,|A|=ℓg(A)+k−1∑i=1f(A△Ai). (6)

If and are both positive polymatroids, then this constitutes an instance of the SH-max problem.

## 3 Properties of the submodular Hamming metric

We next show several interesting properties of the submodular Hamming distance. Proofs for all theorems and lemmas can be found in the supplementary material. We begin by showing that any positive polymatroid function of is a metric. In fact, we show the more general result that any positive normalized monotone subadditive function of is a metric. This result is known (see for instance Chapter 8 of [9]), but we provide a proof (in the supplementary material) for completeness.

###### Theorem 3.1.

Let be a positive normalized monotone subadditive function. Then is a metric on .

###### Proof.

Let be arbitrary. We check each of the four properties of metrics:

1. Proof of non-negativity: because is normalized and positive.

2. Proof of identity of indiscernibles: . The third implication follows because of normalization and positivity of , and the fourth follows from the definition of .

3. Proof of symmetry: , by definition of .

4. Proof of the triangle inequality: First, note that . This follows because each element is either in (true if ) or in (true if ). Similarly, each element is either in (true if ) or in (true if ). Then, because is monotone and subadditive, we have:

 f(A△B)≤f((A△C)∪(C△B))≤f(A△C)+f(C△B). (7)

While these subadditive functions are metrics, their optimization is known to be very difficult. The simple subadditive function example in the introduction of [10] shows that subadditive minimization is inapproximable, and Theorem 17 of [11] states that no algorithm exists for subadditive maximization that has an approximation factor better than . By contrast, submodular minimization is poly-time in the unconstrained setting [12], and a simple greedy algorithm from [13] gives a -approximation for maximization of positive polymatroids subject to a cardinality constraint. Many other approximation results are also known for submodular function optimization subject to various other types of constraints. Thus, in this work we restrict ourselves to positive polymatroids.

###### Corollary 3.1.1.

Let be a positive polymatroid function. Then is a metric on .

This restriction does not entirely resolve the question of optimization hardness though. Recall that the optimization in SH-min and SH-max is with respect to , but that the are applied to the sets . Unfortunately, the function , for a fixed set , is neither necessarily submodular nor supermodular in . The next example demonstrates this violation of submodularity.

###### Example 3.1.1.

To be submodular, the function must satisfy the following condition for all sets : . Consider the positive polymatroid function and let consist of two elements: . Then for and (with ):

 gB(A1)+gB(A2)=√1+√3<2√2=gB(A1∪A2)+gB(A1∩A2). (8)

This violates the definition of submodularity, implying that is not submodular.

Although can be non-submodular, we are interestingly still able to make use of the fact that is submodular in to develop approximation algorithms for SH-min and SH-max.

## 4 Minimization of the submodular Hamming metric

In this section, we focus on SH-min (the centroid-finding problem). We consider the four cases from Table 1: the constrained () and unconstrained () settings, as well as the homogeneous case (where all are the same function) and the heterogeneous case. Before diving in, we note that in all cases we assume not only the natural oracle access to the objective function (i.e., the ability to evaluate for any ), but also knowledge of the (the sequence). Theorem 4.1 shows that without knowledge of , SH-min is inapproximable. In practice, requiring knowledge of is not a significant limitation; for all of the applications described in Section 2, is naturally known.

###### Theorem 4.1.

Let be a positive polymatroid function. Suppose that the subset is fixed but unknown and . If we only have an oracle for , then there is no poly-time approximation algorithm for minimizing , up to any polynomial approximation factor.

###### Proof.

Define as follows:

 f(Y)={0 if Y=∅1 otherwise. (9)

Then unless . Thus, it would take any algorithm an exponential number of queries on to find . ∎

### 4.1 Unconstrained setting

Submodular minimization is poly-time in the unconstrained setting [12]. Since a sum of submodular functions is itself submodular, at first glance it might then seem that the sum of in SH-min can be minimized in poly-time. However, recall from Example 3.1.1 that the ’s are not necessarily submodular in the optimization variable, . This means that the question of SH-min’s hardness, even in the unconstrained setting, is an open question. Theorem 4.2 resolves this question for the heterogeneous case, showing that it is NP-hard and that no algorithm can do better than a -approximation guarantee. The question of hardness in the homogeneous case remains open.

###### Theorem 4.2.

The unconstrained and heterogeneous version of SH-min is NP-hard. Moreover, no poly-time algorithm can achieve an approximation factor better than .

###### Proof.

We first show that for any graph it is possible to construct and such that the corresponding sum in the SH-min problem has minimum value if and only if is a vertex cover for . For constants and , let and . For every edge , define two positive polymatroid functions:

 f1e(Y)=(γ1|Y∩u|+γ2|Y∩v|)1/k,andf2e(Y)=(γ2|Y∩u|+γ1|Y∩v|)1/k. (10)

Let and and define the sum :

 Fc(A)=∑e∈Ehe(A),forhe(A)=f1e(A△B1e)+f2e(A△B2e). (11)

The value of each term in this sum is shown in Table 3. Note that the definition of ensures that .

Using Table 3, we can show that the minimizers of are exactly the set covers of :

• Case 1—show that every vertex cover of is a minimizer of : By the definition of , we know , and so the minimum value of occurs when all are , which is clearly achievable by setting . Any set that is a vertex cover contains at least one endpoint of each edge, and hence also has value for each .

• Case 2—show that every minimizer of is a vertex cover of : Suppose that is a minimizer of but not a vertex cover of . Then there exists some uncovered edge with neither endpoint in . Consider adding to to form a set . The corresponding difference in value is: . The difference in -value for each other edge that touches is similarly if is uncovered in , or if is covered by . All other -values remain unchanged. Thus, , contradicting the assumption that is a minimizer of .

Borrowing from [14]’s Theorem 3.1, we now define a particular graph and two additional positive polymatroid functions. Consider the bipartite graph where and the edge set consists of edges that form a perfect matching of to . Let be a random minimum-cardinality vertex cover of . Define the following two functions:

 fa0(Y)=min{|Y|,r}fb0(Y)=min{|Y∩¯R|+min{|Y∩R|,(1+δ)r2},r} (12)

where is set so that . [14] shows that, knowing but given only value-oracle access to the , no poly-time algorithm can distinguish between and . Moreover, if restricted to vertex cover solutions, it is easy to see that the function is minimized on any of the possible vertex covers, for which it has value , while the function is minimized on the set , for which it has value . The ratio of these minimizers is , which allows [14] to show that no poly-time algorithm can achieve a -approximation for the minimum submodular vertex cover problem.

Now, instead of explicitly restricting to vertex cover solutions, consider unconstrained minimization on and . Since and cannot be distinguished in poly-time, neither can and . We can also show that: (1) any minimizer of or must be a vertex cover, and (2) the ratio of the corresponding vertex cover minimizers is .

• Show ’s minimizers are vertex covers: Suppose that is a minimizer of but not a vertex cover of . Then there exists some uncovered edge with neither endpoint in . Consider adding to to form a set . As shown above, the corresponding difference in value is . The difference is if and otherwise. Thus, all we need is for to be . Plugging in the definition of and , this inequality can be seen to hold for all . Thus, overall , contradicting the assumption that is a minimizer of .

• Show ’s minimizers are vertex covers: The reasoning here is analogous to the case; the difference is always , since adding a single node can never change the value by more than .

• ’s minimum value: Any vertex cover includes at least nodes and thus has value . Since there are edges total, for a vertex cover. Combining these we see that has minimum value .

• ’s minimum value: The vertex cover consisting of the set minimizes : . Thus, the minimum value is .

Letting , we have that . Thus, in the limit the as , the ratio of minimizers is: . Plugging in the definition of from above, the ratio in terms of is: . ∎

Since unconstrained SH-min is NP-hard, it makes sense to consider approximation algorithms for this problem. We first provide a simple -approximation, Union-Split (see Algorithm 1). This algorithm splits into , then applies standard submodular minimization (see e.g. [15]) to the split function. Theorem 4.3 shows that this algorithm is a -approximation for SH-min. It relies on Lemma 4.2.1, which we state first.

###### Lemma 4.2.1.

Let be a positive monotone subadditive function. Then, for any :

 f(A△B)≤f(A∖B)+f(B∖A)≤2f(A△B). (13)
###### Proof.

The upper bound follows from the definition of and the fact that is subadditive:

 f(A△B)=f((A∖B)∪(B∖A))≤f(A∖B)+f(B∖A). (14)

The lower bound on follows due to the monotonicity of : and . Summing these two inequalities gives the bound. ∎

###### Theorem 4.3.

Union-Split is a -approximation for unconstrained SH-min.

###### Proof.

An SH-min instance seeks the minimizer of . Define . From Lemma 4.2.1, we see that is a -approximation for (any submodular function is also subadditive). Thus, if can be minimized exactly, the result is a -approximation for SH-min. Exact minimization of is possible because is submodular in . The submodularity of follows from the fact that submodular functions are closed under restriction, complementation, and addition (see [16], page 9). These closure properties imply that, for each , and are both submodular in , as is their sum. ∎

Note that Union-Split’s -approximation bound is tight; there exists a problem instance where exactly a factor of is achieved. More concretely, consider , , , and for . Then according to the passed to Submodular-Opt, all solutions have value . Yet, under the true the solutions and have the better (smaller) value . Letting , the quantity approaches , making the ratio between the correct solution and the one given by Union-Split possibly as large as .

Restricting to the homogeneous setting, we can provide a different algorithm that has a better approximation guarantee than Union-Split. This algorithm simply checks the value of for each and returns the minimizing . We call this algorithm Best-B (Algorithm 2). Theorem 4.4 gives the approximation guarantee for Best-B. This result is known [17], as the proof of the guarantee only makes use of metricity and homogeneity (not submodularity), and these properties are common to much other work. We provide the proof in our notation for completeness though.

###### Theorem 4.4.

For , Best-B exactly solves unconstrained SH-min. For , Best-B is a -approximation for unconstrained homogeneous SH-min.

###### Proof.

Define , for positive polymatroid. Since each is normalized and positive, each is minimized by : . Thus, any given is minimized by setting . For , this implies that SH-min is exactly solved by setting .

Now consider and the homogeneous setting where there is a single : . By Theorem 3.1, is a metric, so it obeys the triangle inequality:

 f(A△Bi)+f(A△Bj)≥f(Bi△Bj)∀i,j. (15)

Fixing some and summing this inequality over all :

 ∑j≠i[f(A△Bi)+f(A△Bj)]≥∑j≠if(Bi△Bj)=m∑i=1f(Bi△Bj) (16)

where the last equality is due to the fact that polymatroids are normalized: . Regrouping terms, is independent of , so it can be pulled out of the summation:

 (m−2)f(A△Bi)+m∑j=1f(A△Bj)≥m∑j=1f(Bi△Bj). (17)

Notice that is exactly and is . Substituting in this notation and summing over all :

 m∑i=1[(m−2)f(A△Bi)+F(A)]≥m∑i=1F(Bi). (18)

On the left-hand side we can again replace the sum with , yielding: . Since a sum over items is larger than times the minimum term in the sum, the remaining sum here can be replaced by a min:

 2(m−1)F(A)≥mmini∈{1,…,m}F(Bi). (19)

The left-hand size is exactly what the Best-B algorithm computes, and hence the minimizing found by Best-B is a -approximation for unconstrained homogeneous SH-min. ∎

Note that as a corollary of this result, in the case when , the optimal solution for unconstrained homogeneous SH-min is to take the best of and . Also note that since Union-Split’s -approximation bound is tight, Best-B is theoretically better in terms of worst-case performance in the unconstrained setting. However, Union-Split’s performance on practical problems is often better than the Best-B’s, as many practical problems do not hit upon this worst case. For example, consider the case where , is simply cardinality, , and each consists of two items: . Then the best has -value , while the set found by Union-Split has a lower (better) -value of .

### 4.2 Constrained setting

In the constrained setting, the SH-min problem becomes more difficult. Essentially, all of the hardness results established in existing work on constrained submodular minimization applies to the constrained SH-min problem as well. Theorem 4.5 shows that, even for a simple cardinality constraint and identical (homogeneous setting), not only is SH-min NP-hard, but also it is hard to approximate with a factor better than .

###### Theorem 4.5.

Homogeneous SH-min is NP-hard under cardinality constraints. Moreover, no algorithm can achieve an approximation factor better than , where denotes the curvature of . This holds even when .

###### Proof.

Let and . Then under cardinality constraints, SH-min becomes . Corollary 5.1 of [18] establishes that this problem is NP-hard and has a hardness of . ∎

We can also show similar hardness results for several other combinatorial constraints including matroid constraints, shortest paths, spanning trees, cuts, etc. [18, 14]. Note that the hardness established in Theorem 4.5 depends on a quantity , which is also called the curvature of a submodular function [19, 18]. Intuitively, this factor measures how close a submodular function is to a modular function. The result suggests that the closer the function is being modular, the easier it is to optimize. This makes sense, since with a modular function, SH-min can be exactly minimized under several combinatorial constraints. To see this for the cardinality-constrained case, first note that for modular , the corresponding -function is also modular. Lemma 4.5.1 formalizes this.

###### Lemma 4.5.1.

If the in SH-min are modular, then is also modular.

###### Proof.

Any normalized modular function can be represented as a vector , such that . With , this can be written:

 fi(A△Bi)= w⊤i[1Bi+diag(1V∖Bi−1Bi)1A] (20) = ∑j∈Biwi(j)+∑j∈A(−1)1(j∈Bi)wi(j). (21)

Summing over and letting represent the part that is constant with respect to , we have:

 F(A)=C+∑j∈A(−1)1(j∈Bi)wi(j). (22)

Thus, can be represented by offset and vector such that , with entries . This is sufficient to prove modularity. (For optimization purposes, note that can be dropped without affecting the solution to SH-min.) ∎

Given Lemma 4.5.1, from the definition of modularity we know that there exists some constant and vector , such that . From this representation it is clear that can be minimized subject to the constraint by choosing as the set the items corresponding to the smallest entries in . Thus, for modular , or with small curvature , such constrained minimization is relatively easy.

Having established the hardness of constrained SH-min, we now turn to considering approximation algorithms for this problem. Unfortunately, the Union-Split algorithm from the previous section requires an efficient algorithm for submodular function minimization, and no such algorithm exists in the constrained setting; submodular minimization is NP-hard even under simple cardinality constraints [20] (although see [21] that shows it is possible to get solutions for a subset of the cardinality constraints). Similarly, the Best-B algorithm breaks down in the constrained setting; its guarantees carry over only if all the are within the constraint set . Thus, for the constrained SH-min problem we instead propose a majorization-minimization algorithm. Theorem 4.6 shows that this algorithm has an approximation guarantee, and Algorithm 3 formally defines the algorithm.

Essentially, Major-Min proceeds by iterating the following two steps: constructing , a modular upper bound for at the current solution , then minimizing to get a new . consists of superdifferentials [22, 23] of ’s component submodular functions. We use the superdifferentials defined as “grow” and “shrink” in [24]. Defining sets as for “grow”, and for “shrink”, the vector that represents the modular can be written:

 w^F(j)=m∑i=1{fi(j∣S)ifj∈A△Bifi(j∣T)otherwise, (23)

where is the gain in -value when adding to . We now state the main theorem characterizing algorithm Major-Min’s performance on SH-min.

###### Theorem 4.6.

Major-Min is guaranteed to improve the objective value, , at every iteration. Moreover, for any constraint over which a modular function can be exactly optimized, it has a approximation guarantee, where is the optimal solution of SH-min.

###### Proof.

We first define the full “grow” and “shrink” superdifferentials:

 mfA,1(Y) ≜f(A)− ∑j∈A∖Yf(j∣V∖j)+∑j∈Y∖Af(j∣A),and (24) mfA,2(Y) ≜f(A)−∑j∈A∖Yf(j∣A∖j)+∑j∈Y∖Af(j∣∅). (25)

When referring to either of these modular functions, we use . Note that the upper-bound in the following sense: , and .

Major-Min proceeds as follows. Starting from and applying either “grow” or “shrink” to construct a modular approximation to at yields the following simple surrogate function for each : . The below bound then holds (from [18]):

 fi(Y)≤^fi(Y)≤|Y|1+(|Y|−1)(1−κfi(Y))fi(Y),∀Y⊆V. (26)

Let . Also, let . Then, it holds that:

 m∑i=1fi(^A△Bi) ≤m∑i=1^fi(^A△Bi) (27) ≤m∑i=1^fi(A∗△Bi) (28) ≤|A∗|1+(|A∗|−1)(1−κfi(A∗))m∑i=1fi(A∗△Bi) (29)

The first inequality follows from the definition of the modular upper bound, the second inequality follows from the fact that is the minimizer of the modular optimization, and the third inequality follows from Equation 26. We now show that Major-Min improves the objective value at every iteration:

 m∑i=1fi(At+1△Bi)≤m∑i=1mfiAt△Bi(At+1△Bi)≤m∑i=1mfiAt△Bi(At△Bi)=m∑i=1fi(At△Bi). (30)

While Major-Min does not have a constant-factor guarantee (which is possible only in the unconstrained setting), the bounds are not too far from the hardness of the constrained setting. For example, in the cardinality case, the guarantee of Major-Min is , while the hardness shown in Theorem 4.5 is .

## 5 Maximization of the submodular Hamming metric

We next characterize the hardness of SH-max (the diversification problem) and describe approximation algorithms for it. We first show that all versions of SH-max, even the unconstrained homogeneous one, are NP-hard. Note that this is a non-trivial result. Maximization of a monotone function such as a polymatroid is not NP-hard; the maximizer is always the full set . But, for SH-max, despite the fact that the are monotone with respect to their argument , they are not monotone with respect to itself. This makes SH-max significantly harder. After establishing that SH-max is NP-hard, we show that no poly-time algorithm can obtain an approximation factor better in the unconstrained setting, and a factor of in the constrained setting. Finally, we provide a simple approximation algorithm which achieves a factor of for all settings.

###### Theorem 5.1.

All versions of SH-max (constrained or unconstrained, heterogeneous or homogeneous) are NP-hard. Moreover, no poly-time algorithm can obtain a factor better than for the unconstrained versions, or better than for the cardinality-constrained versions.

###### Proof.

We first show that homogeneous unconstrained SH-max is NP-hard. We proceed by constructing an that can represent any symmetric positive normalized (non-monotone) submodular function. Maximization is NP-hard for this type of function, since it subsumes the Max-Cut problem. Hence, the reduction to unconstrained SH-max suffices to show NP-hardness.

Consider an instance of SH-max with and