1 Introduction
Submodularity naturally arises in a number of machine learning problems, such as active learning
[10], clustering [22], and dictionary selection [5]. The scalability of submodular optimization methods is critical in practice, thus has drawn much attention from the research community. For example, Iyer et al. [12] propose general optimization methods based on the semidifferential. Wei et al. [25] combine approximation with pruning to accelerate the greedy algorithm for uniform matroid constrained submodular maximization. Mirzasoleiman et al. [21] and Pan et al. [24] use distributed implementation to accelerate existing optimization methods. Other techniques, including stochastic sampling [20] and decomposable assumption [14], are also applied to scale up submodular optimization methods. While in this paper, we focus on the reducibility of submodular functions, a favourable property that can substantially improve the scalability of submodular optimization methods. The reducibility can directly reduce the solution space of the submodular optimization problems, while preserve all the optima in the reduced space, thereby enables us to accelerate the optimization process without incurring performance loss.Recent research shows that for some submodular functions, by evaluating marginal gains, reduction can be applied for unconstrained maximization [8], unconstrained minimization [6, 12], and uniform matroid constrained maximization [25]. By leveraging the reducibility, a variety of methods have been developed to scale up the optimization of reducible submodular functions [6, 8, 12, 19], While existing works mainly focus on reducible functions, there exist a number of irreducible submodular functions widely applied in practice, for which existing methods can only provide vacuous reduction.
In this paper, we investigate the problem that whether irreducible functions can also exploit this favorable property. We firstly introduce the concept of reducibility using marginal gains over the endpoint sets of a given lattice. Then for irreducible functions, we transform them to reducible functions by adding random noise to perturb the marginal gains, after which we perform lattice reduction for the perturbed functions and solve the original functions on the reduced lattice. Theoretical results show that given the perturbation scales, the reducibility gain is lower bounded, and the performance loss has additive upper bounds. The empirical results demonstrate that there exist useful perturbation scale intervals in practice, which enables us to significantly accelerate existing optimization methods with small performance losses.
In summary, this paper has the following contributions. Firstly, we introduce the concept of reducibility, and propose the perturbationreduction framework. Secondly, we theoretically analyze our proposed method. In particular, for the reducibility gain, we propose a lower bound in terms of the perturbation scale. For the performance loss, we propose both deterministic and probabilistic upper bounds. The deterministic bound provides the understanding of relationship between the reducibility gain and performance loss, while the probabilistic bound can explain the experimental results. Finally, we empirically show that the proposed method is applicable for a variety of commonly used irreducible submodular functions.
In the sequel, we organize the paper as follows. In Section 2, we introduce the definitions and the existing reduction algorithms. In Section 3, we propose our perturbation based method. Theoretical analysis and empirical results are presented in Section 4 and Section 5, respectively. In Section 6, we review some related works. Section 7 comes to our conclusion.
2 Reducibility
2.1 Notations and Definitions
Given a finite set , and a set function . is said to be submodular [6] if , . An equivalent definition of submodularity is the diminishing return property: , , , where is called the marginal gain of element with respect to set . To simplify the notation, we denote by , and by . Given , the (set interval) lattice is defined as .
Suppose , and is a submodular function. In this paper, we focus on unconstrained submodular optimization problems,
Problem 1 can be exactly solved in polynomial time [23], while Problem 2 is NPhard since some of its special cases (e.g., Max Cut) are NPhard.
For convenience of the presentation, we will use P1 and P2 to refer to Problem 1 and Problem 2, respectively. Similarly, for the following algorithms, we will use A1 and A2 to refer to Algorithm 1 and Algorithm 2. The reference holds for P3, P4, A3 and A4.
Define as the optima set of P1. Similarly, define . Obviously, we have and .
We now give the definition of reducibility. For P1 (P2), we say the objective function is reducible for minimization (maximization) if , where can be obtained in function evaluations, such that (). Note that if we can only find in time, the reduction is meaningless since time is enough for us to find all the optima. The ratio is called the reduction rate.
2.2 Algorithms
Existing works on reduction for unconstrained submodular optimization can be summarized by the following two algorithms, both of which terminate in time. The brief review of existing works can be found in Section 6.
Proposition 1.
Suppose is submodular. After each iteration of A1 (A2), we have ().
We prove Proposition 1 in the supplementary material. According to Proposition 1, if the output of A1 (A2) statifies , then is reducible.
According to A1 (A2), if , then we have and . The algorithm will terminate after the first iteration and the output is , which provides a vacuous reduction. In this case, we say that is irreducible with respect to A1 (A2). For convenience, we directly say is irreducible.
Thereby, we conclude two points from the above algorithms. First, by the definition of and , the reducibility of can be determined by the signs of marginal gains with respect to the endpoint sets of the current working lattice. Second, the reducibility of for minimization and maximization are actually the same property. Specially, suppose in a certain iteration, A1 and A2 have the same working lattice . According to the algorithms, they also have the same and , which determine whether is reducible after the current iteration.
Proposition 2.
Given a submodular function , and a lattice . , Define . Then is reducible on with respect to A1 (A2) if and only if
(1) 
Proof.
Suppose in A1 (A2). Then is reducible if and only if the algorithm does not terminate after its first iteration, i.e., or . Suppose happens, i.e., , . According to submodularity, . We have . Suppose happens, i.e., , . According to submodularity, . We have . ∎
3 Perturbation Reduction
Given a reducible submodular function, we can use A1 and A2 to provide useful reduction. Unfortunately, there still exist many irreducible submodular functions, some of which are listed in the experimental section. Given a submodular function , which is irreducible on . According to Proposition 2, , we have and .
If we expect A1 and A2 to provide nontrivial reduction, we need to guarantee that (1) holds for some elements without changing the submodularity of the objective function. A natural way is to add random noise ^{1}^{1}1 is a modular function, and . to perturb the original function as follows,
where , is generated uniformly at random in for some . By appropriately choosing the value of , we can ensure or hold for some . Thus we have (1) holds, indicating that is reducible. At the same time, as is a modular function, the submodularity of still holds.
We propose our perturbation based method for minimization and maximization in A3 and A4, respectively. For an irreducible submodular function on a given lattice , we first perturb the objective function to make it reducible, i.e., . A1 or A2 are then employed to obtain the reduced lattice of . Finally we solve the original problems of on the reduced lattice exactly or approximately using existing methods.
It is worth mentioning that, though we mainly focus on irreducible functions, our methods also work for reducible ones, as they are special cases of irreducible functions. Particularly, given a reducible function on , of which the reduction rate is less than , after A1 (A2) terminates, we can get a sublattice so that is irreducible on .
4 Theoretical Analysis
By perturbing the irreducible submodular function, we transform P1 (P2) into P3 (P4). This makes the objective reducible while leads the solution to be inexact. Correspondingly, our theoretical analysis of the method focuses on two main aspects: the reducibility gain and the performance loss incurred by perturbation.
4.1 Reducibility Gain
Suppose is an irreducible submodular function on , and as defined in P3 and P4. Since is irreducible, , we have and .
Proposition 3.
Given a submodular function , which is irreducible on . Define . If , then is irreducible on .
Proof.
Since , we suppose . , we have , and , which implies that is also irreducible on . ∎
Proposition 3 indicates that if the perturbation scale is small enough, there is no reducibility gain. This is intuitively reasonable since we have when .
To lower bound the reducibility gain of adding perturbation, we generalize the concept of curvature [4, 13] for nonmonotone irreducible submodular functions.
Definition 1.
Given a submodular function , the curvature of on is defined as,
Note that for any irreducible submodular function on , we have .
Theorem 1.
Suppose , denote , , . The reduction rate in expectation of is at least .
Proof.
Suppose ,
, we define a random variable
as,indicates whether can be reduced from or not. Define as the total number of the reduced elements. We firstly lower bound by the total number of the reduced elements after the first iteration round of A1(A2),
Consequently, the reduction rate in expectation is . ∎
Theorem 1 implies that the reduction rate in expectation approaches as the perturbation scale increases. This is also consistent with our intuition since when . Note that is a modular function, which always has the highest reduction rate .
4.2 Performance Loss
Suppose (), i.e., () is an optimum of P1 (P2). Recall that () is the output of A3 (A4), for P1 (P2), we define () as the performance loss incurred by perturbation. For P1 (P2), the following result shows that the performance loss is upper bounded by the total perturbation of the “mistakenly” reduced elements, which will be explained later on.
Theorem 2.
Given an irreducible submodular function . Suppose is the perturbation scale in A3 (A4), and is the reduction rate. We have,
Proof.
We prove the maximization case. In general, we have , otherwise the loss is zero. Note that according to A4. So we firstly introduce an intermediate set , i.e., the contraction of in for our analysis. Given the fact that , if we can upper bound , then the total performance loss is also upper bounded. In A2, we have . By definition, , , and . We have,
(2)  
(3)  
(4)  
(5)  
(6) 
where (3) is the telescopic version of (2). According to submodularity, we have (4) holds, and (5) comes from the third step of A2. Similarly, we have,
(7) 
Combining (6) with (7), and noting and , we have,
(8) 
We note in (8), is actually the set of all the elements which are not in but added by A2. Similarly, is the set of all the elements which are in but eliminated by A2. Consequently, the performance loss is upper bounded by the total perturbation value of all the mistakenly reduced elements. Since the number of all the mistakenly reduced elements is no more than the number of all the reduced elements , and the perturbation is generated in , we have .
For the minimization case, the proof is similar. ∎
Note that in Theorem 2
, the performance loss is upper bounded by the sum of random variables, which means we can obtain high probability bounds using some concentration inequalities, such as
[11].Theorem 3.
(Hoeffding) Let be independent realvalued random variables such that , . Then with probability ,
Theorem 4.
Define , and . Denote , and , where is the symmetric difference between the two sets and . Then with probability at least ,
(9)  
(10) 
Proof.
We prove (10
). Since the perturbation vector
has zero expectation value, and each element of is independently generated. For any fixed , according to Theorem 3, with probability at least ,(11) 
Suppose , and define . Obviously, we have . Hence,
where the first equality holds by the law of total probability. The second equality holds because replacing
with in the first expression does not change the event. The first inequality comes from dropping the event increases the probability. The last line results from (11) and the definition of . Combining the above result with Theorem 2, and note , we have, with probability at least ,Using a similar method, (9) can also be proved. ∎
Theorem 4 has an intuitive interpretation. Take P2 and P4 as examples, , if is large, then it is unlikely that is an optimum of P4. Suppose , then we have,
Totally, the probability of being an optimum of P4 is upper bounded by the probability that the perturbation difference can compensate the function value difference , where the later probability is small when is large.
Finally, we show that the and in Theorem 4, which are the numbers of mistakenly reduced elements, can also be upper bounded by functions of and .
Theorem 5.
Denote the total number of the mistakenly reduced elements in the first iteration of A1 and A2 as and , respectively. We have,
(12)  
(13) 
where .
Proof.
For (13), we calculate the total mistakenly reduced element number in expectation in the first iteration of A2. According to the definition of symmetric difference, .
Using similar methods we can obtain the following results, which recover Theorem 5 as a special case.
Theorem 6.
Denote the total number of the mistakenly reduced elements in the th iteration as , and , respectively. We have,
where , and .
Theorem 5 implies that the expected number of mistakenly reduced elements in the first iteration will approach as the perturbation scale increases. This is consistent with the intuition. Let , then . Each element will be randomly selected to be added or eliminated with probability , so the expected number of mistakenly reduced elements is .
Remark 1.
When is large enough, most elements will be reduced in the first iteration, i.e., . Let , where , by (13) we have , which indicates that if is a large enough perturbation scale, then the number of mistakenly reduced elements can be desirably upper bounded.
Remark 2.
Suppose is large enough and . With the result of Theorem 2, we have
Let where , we have from above. This means if there is some relationship between the optimum and the perturbation ( is a large perturbation scale), then the previous performance loss results can be transformed into approximation ratios.
5 Experimental Results
For reducible submodular functions, by incorporating reduction into optimization methods, favorable performance has been achieved [6, 8, 12, 19]. In our experiments, we mainly focus on (nearly) irreducible submodular functions, as listed below.
Subset Selection Function.
Mutual Information Function.
Given random vectors , define as the entropy of random variables , which is a highly reducible submodular function. The symmetrization [1] of leads to the mutual information , which is irreducible. We set , and randomly generate .
LogDeterminant Function.
Given a positive definite matrix , the determinant [17] is logsubmodular. The symmetrization of logdeterminant is , where , . We set . We randomly generate data points and compute the similarity matrix as the positive definite matrix .
Negative HalfProducts Function.
5.1 Perturbation Scale
In Theorem 1, we lower bound the expectation of reduction rate using the expectation of reduction rate after the first iteration of A1 (A2). It is recently reported that for reducible submodular minimization, this bound is often not tight in practice [12]. Given that our method is actually transforming irreducible functions to reducible ones, it is reasonable to borrow experience from reducible cases. We conjecture that relatively small reduction rates after the first iteration would be sufficient for high reduction rates after the last iteration, thereby we only need to choose small perturbation scales to obtain desirable reducibility gains. We empirically verify the conjecture as shown in Figure 1. Appropriate perturbation scales are chosen so that the reduction rates after the last iteration are changing from to nearly . Given a certain perturbation scale, we repeatedly generate for times and record the average reduction rates of A2 after iteration  and the last iteration. We observe that A2 terminates within iterations for all objective functions.
We defer similar results for minimization to the supplementary material. As conjectured, we learn from Figure 1 that the gap between the average reduction rates after the first iteration and the last iteration is always large in practice. Hence, we can choose to get appropriate reduction rates in expectation (e.g., ) after the first iteration, so as to obtain potentially high final reduction rates. Although we can empirically utilize the gap of reducibility gain to choose relative small perturbation scales, we would like to point out that theoretically determining the reduction rates in expectation after the last iteration given certain perturbation scales is still an open problem.
5.2 Optimization Results
We implement our method using SFO toolbox [15].
For maximization, we compare A4 with both exact and approximate methods, as exact methods usually cannot terminate in acceptable time with larger input scales. Denote the outputs of the proposed A4 and the existing method as and , respectively. Also denote the running time as and . We measure the performance loss using relative error, which is defined as . When is exact, is the approximation ratio. We measure the reducibility gain using both the reduction rate and the time ratio . Small time ratios and relative errors indicate large reducibility gains and small performance losses, respectively.
We employ the branchandbound method [9] as the exact solver. Since it has exponential time complexity, we reset so that it terminates within acceptable time. The results are shown in Figure 2. For comparison, we normalize the perturbation scale as follows. We define , and define the perturbation scale ratio as . We change the perturbation scale in by varying in . We then randomly generate cases for each objective function and record the average relative errors, average reduction rates, and average time ratios for each perturbation scale ratio. Figure 3 shows the results compared with the random bidirectional greedy method [3], which is used as the approximate solver. Note that is set to 100. For each case, we firstly run A2 once, and then run the random method times on both the original and the reduced lattice, and record the best solutions.
According to Figure 2 and Figure 3, when the perturbation scale ratio is smaller than , the time ratio is larger than . This is because the small reducibility gain cannot make the combination methods more efficient than before. As the perturbation scale ratio increases, the reduction rate increases and the time ratio decreases as expected. Meanwhile, the relative error increases gently when increases, indicating that there exist useful intervals, in which the perturbation scales can lead to large reducibility gains and small performance losses.
For minimization, since the subset selection and the mutual information function have trivial zero optimal values, i.e., , we use the later two as objective functions. We employ the FujishigeWolfe minimumnorm point algorithm [7] as the exact solver. All the settings are the same as those of maximization. The results of minimization are shown in Figure 4. We note that for negative halfproducts function, the useful interval of perturbation scales is smaller than those of other functions. According to Remark 2, as the marginal gains are relatively large compared to the optimal value, it is inappropriate to choose large perturbation scales in this case.
6 Related Work
In this section, we review some existing works related to solution space reduction for submodular optimization. For P1, Fujishige [6] firstly proves , where and . Note that actually , which is the working lattice of A1 after its first iteration. Recently, Iyer et al. [12] propose the discrete MajorizationMinimization (MMin) framework for P1. They prove that by choosing appropriate supergradients, MMin is identical with A1. For P2, Goldengorin [8] proposes the Preliminary Preservation Algorithm (PPA), which is identical with A2. For general cases, Mei et al. [19] prove that the two algorithms work for quasisubmodular functions. Beyond unconstrained problems, for uniform matroid constrained monotone submodular function optimization, Wei et al. [25] propose similar pruning method in which the reduced ground set contains all the original solutions of the greedy algorithm.
7 Conclusions
In this paper, we introduce the reducibility of submodularity, which can improve the efficiency of submodular optimization methods. We then propose the perturbationreduction framework, and demonstrate its advantages theoretically and empirically. We analyze the reducibility gain and performance loss given perturbation scales. Experimental results show that there exists practically useful intervals, and choosing perturbation scales from them enables us to significantly accelerate the existing methods with only small performance loss. For the future work, we would like to study the reducibility of submodular functions in constrained problems.
Acknowledgements
Jincheng Mei would like to thank Csaba Szepesvári for fixing the proof of Theorem 4. BaoLiang Lu was supported by the National Basic Research Program of China (No. 2013CB329401), the National Natural Science Foundation of China (No. 61272248) and the Science and Technology Commission of Shanghai Municipality (No. 13511500200). Asterisk indicates the corresponding author.
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Appendix
Appendix A Proof of Proposition 1
For Algorithm 1, the proof can be found in [12]. For Algorithm 2, the proof can be found in [8]. A proof using weaker assumption of quasisubmodular function can be found in [19]. We prove Proposition 1 here for completeness.
Proof.
Algorithm 1. Obviously . Suppose , we now prove . Suppose is a minimum of , then we have . For , if , by submodularity, we have , i.e., , which contradicts with the optimality of . So we have , and . , if , by submodularity, we have , i.e., , which also contradicts with the optimality of . Therefore we have , and
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