Scaled relative graphs for pairs of operators beyond classical monotonicity Thanks: This work was supported by the Research Foundation Flanders (FWO) PhD grant 11A8T26N and research projects G081222N, G033822N, and G0A0920N; Research Council KUL grant C14/24/103.
Abstract
We introduce a generalization of the scaled relative graph (SRG) to pairs of operators, enabling the visualization of their relative incremental properties. This novel SRG framework provides the geometric counterpart for the study of nonlinear resolvents based on paired monotonicity conditions. We demonstrate that these conditions apply to linear operators composed with monotone mappings, a class that notably includes NPN transistors, allowing us to compute the response of multivalued, nonsmooth, and highly nonmonotone electrical circuits.
I Introduction
Understanding the input-output behavior of systems and their underlying operators is a central problem in the study of dynamical systems and control algorithms. In this context, classical monotone operator theory provides a unifying mathematical framework for modeling feedback interconnections, optimization dynamics, and equilibrium systems. Moreover, monotonicity is a fundamental property for ensuring stability and convergence in many settings, from proximal algorithms to feedback systems governed by maximal monotone mappings. However, many relevant control and learning systems are described by nonmonotone operators and thus require a different approach.
A new concept, pair monotonicity, which is a specialization of the -monotonicity concept from [4, Def. 3], has been introduced in order to better analyze the differential inclusions of sweeping processes in [1]. Notably, a set-valued variant of this monotonicity property was also used in [16] to characterize the firm nonexpansiveness of generalized resolvent operators. Departing from standard monotonicity that links the inputs and outputs of an operator, pair monotonicity describes the incremental properties of the output of one operator compared to the output of another, thus making it useful for characterizing highly nonmonotone systems. Pair monotonicity provides a powerful way to incorporate prior knowledge by choosing the second operator judiciously. As this approach is purely algebraic, this choice may be quite difficult in practice.
Rather than relying on algebra, graphical tools are invaluable for building intuition. To this end, the scaled relative graph (SRG) [22] has emerged as a powerful framework by mapping the action of operators onto the (extended) complex plane. Serving as a nonlinear generalization of the classical Nyquist diagram, this tool has since seen applications in various systems and control contexts, such as graphical system analysis [7, 2, 9, 13] and reset control systems [23], and has proven useful in the convergence analysis of various algorithms [17, 22].
Originally, the SRG was used for the analysis of operator properties, where the high-level approach is shown in Figure 1. Of particular interest is showing that operators are firmly nonexpansive or contractive, upon which convergence of the associated fixed-point iteration can be shown using the Krasnosel’skiǐ–Mann [3, Cor. 5.17] and Banach fixed-point theorem [3, Thm. 1.50], respectively. With the emergence of the new pair monotonicity, it is natural to ask how the SRG can be extended to handle these novel properties. This is the main subject of study in this paper.
Concretely, our contribution is threefold.
- (i)
We introduce a scaled relative graph for pairs of operators and establish important calculus rules. Furthermore, we extend the notions of SRG-fullness and semimonotonicity to this setting, thereby extending classical monotonicity.
- (ii)
We apply this novel tool to provide purely geometric proofs for the core properties of two nonlinear resolvents that have been used to solve inclusion problems with pairs of monotone operators.
- (iii)
We show the practical utility of this paired monotonicity condition by analyzing operators that can be written as the composition of a linear operator with a monotone mapping, a class that includes nonlinear transistor models. Leveraging this property, we are able to compute the response of a nonmonotone common-emitter amplifier circuit.
I-A Notation
In the following, denotes a real Hilbert space with inner product and induced norm . We denote the sets of complex and extended-complex numbers by and , respectively. As in [22], we avoid , , , and otherwise adopt the convention , , , and . For subsets , set addition is understood as . Scalar multiplication by is defined by . For , denotes its complex conjugate whereas, for a bounded, linear operator , denotes its adjoint. We use to denote the direct sum. For a set-valued mapping , we define its domain , its range and its graph . The inverse mapping , which always exists, is defined by . The set of zeros is denoted by and the set of fixed points by . is firmly nonexpansive if , , -Lipschitz with if , and contractive if it is -Lipschitz with . The composition of two set-valued mappings is defined by for . We denote the identity operator by . The closed disk with center and radius is defined as . We denote the half-plane with . Lastly, a set is said to satisfy the chord property if , cf. [22, Fig. 6].
II Scaled relative graphs of operator pairs
We aim to study the relative incremental properties of an operator compared to another operator . One possible approach is to analyze the composed operator . However, this perspective has several disadvantages: it requires working with explicit operator inverses and compositions, it is more difficult to derive calculus rules, and it may be difficult to preserve problem structure. We therefore take a second approach and study relative incremental properties of operator pairs .
To this end, we introduce the notion of scaled relative graphs for pairs of operators , which we show is equivalent to the classical scaled relative graph [22] of . In particular, let with and define the corresponding complex-conjugate pair
where the angle is defined as if and , and otherwise. The SRG of a pair of operators then consists of the union of these pairs, where and are evaluated at the same inputs.
Definition II.1 (SRG of a pair of operators).
For , the scaled relative graph of a pair of operators is
for all such that . Additionally, includes if there exist and such that (including when ) and . Furthermore, we define the SRG of a class of operator pairs as .
Remark II.1.
The classical scaled relative graph [22] of is recovered for .
Remark II.2.
We now derive some basic identities that follow from the definition and similar proof techniques as in [22]. The main difference arises from not necessarily being bijective, so handling the case requires extra care.
Proposition II.1 (Basic calculus).
Let , and . Then,
- (i)
.
- (ii)
- (iii)
.
- (iv)
.
- (v)
If either or satisfies the chord property, then .
- (vi)
If either or satisfies the chord property, then .
If one of the operators is single-valued or satisfies some mild additional properties, even more calculus rules can be established.
Proposition II.2 (Additional calculus).
Let and suppose is single-valued. Then,
- (i)
If is not constant, then .
- (ii)
.
- (iii)
.
- (iv)
with equality if is surjective.
- (v)
If and for some , then .
- (vi)
If and for some , then .
- (vii)
If is a bounded, invertible linear operator on and , then .
Remark II.3.
A crucial property in the context of the original SRG is the concept of SRG-fullness of operator classes [22, Sec. 3.3], since these allow for membership checking based on geometric containment of the SRG, which forms the final step of the approach shown in Figure 1. A generalization to our framework is straightforward.
Definition II.2 (SRG-full operator classes of pairs).
A class of operator pairs is SRG-full if
Similar to [22, Thm. 2], classes defined through some nonnegatively homogeneous function , i.e., for all , satisfy this desirable property. In fact, if is SRG-full, then such a nonnegatively homogeneous function always exists.
Proposition II.3.
Let be a class of operator pairs. Then is SRG-full if and only if there exists some nonnegatively homogeneous function such that if and only if :
| (1) |
Remark II.4.
Note that the choice of representing a class is not unique, and that there may exist representations that are not nonnegatively homogeneous, e.g., , , and all represent (pair of) monotone operators, but clearly violates the preceding homogeneity condition.
In the context of the original SRG, one particularly interesting SRG-full class is the one defined by , which covers -semimonotone operators, see [20, Prop. 3.2], a class that was introduced in [11, Def. 4.1]. In the following, we generalize this class to the setting of operator pairs, inspired by the operator-pair monotonicity introduced in [4, Def. 3], [16, Eq. (5)]. Note that classical -semimonotonicity is recovered by taking .
Definition II.3 (Semimonotone operator pairs).
Let and . Then is -semimonotone if The class of all -semimonotone operator pairs is denoted by .
We also denote for the -monotone pairs of operators. When and , we recover the class of -strongly monotone operators, while we recover the class of -hypomonotone operators when , similarly to [11, Rem. 4.2]. The SRG of the class of -semimonotone operator pairs is now shown to be exactly the same as in [20, Prop. 3.4], for which we moreover derive an alternative representation.
Proposition II.4 (SRG of semimonotone operator pairs).
Let . Then, is SRG-full. Moreover,
In particular,
| (2) |
Proof sketch.
The forward inclusion is shown using the definition of semimonotone operator pairs, while the reverse inclusion follows because classical semimonotone operators (i.e., with ) are contained in , so that [20, Prop. 3.4] applies. ∎
Example II.1.
Consider the linear operator from [22, Fig. 5]. Figure 2 visualizes, for different operators , the numerical SRG of the pair . Here and henceforth, numerical SRGs are computed with each coordinate sampled independently and uniformly from . For , this reduces to the standard SRG of . Inspired by [16, Lem. 5.1], we also consider the choice , where and is the smallest eigenvalue of in absolute value. Finally, we also consider .
It is clear that is not monotone by (2). Further, the second and third SRGs suggest that , though this cannot be concluded by sampling alone. That they indeed have this property follows from [16, Lem. 5.1] and IV.1(i), respectively.
Example II.2.
Let be the NPN transistor modeled by the Ebers–Moll model, see [20, Sec. 4.2],
where , , and is a diode model satisfying . Figure 3 shows, for different operators , the numerical SRGs of the pairs . For , we observe that the NPN transistor is angle-bounded [20, Def. 3.6], as proven in [20, Prop. 4.4], and that it is not monotone by (2). Unlike in example II.1, the choice , where and is the smallest eigenvalue of in absolute value [16, Lem. 5.1], does not yield a monotone pair of operators. Finally, for , the sampled SRG suggests monotonicity of the pair , which Corollary IV.1 later establishes formally.
III Properties of nonlinear resolvents
Let and consider the zero inclusion problem
that arises ubiquitously in optimization and systems theory. A classical way to solve this problem is to pose it as finding a fixed point of a related operator. The most well-known example is the resolvent , which leads to the celebrated proximal point algorithm. Often, this fixed point operator is shown to be firmly nonexpansive or contractive, from which convergence readily follows by the Krasnosel’skiǐ–Mann or Banach fixed-point theorem.
We now recall two nonlinear resolvents called the warped resolvent [5] and the transformed resolvent [16].
Definition III.1 (Nonlinear resolvents).
Let and suppose is single-valued. The transformed resolvent of with respect to is defined as . The warped resolvent of with respect to is defined as , provided that .
These resolvents are useful since and in light of [16, Prop. 3.2]. Therefore, if we can show firm nonexpansiveness or contraction, then standard theory shows convergence, as described above. These properties have indeed been shown under the pair of monotonicity framework in [16]. For the remainder of this paper, we assume that the considered resolvents are everywhere defined on the relevant space.
We now provide the geometric picture of these derivations by giving purely SRG-based proofs. This approach yields concise proofs that capture the core insights and also allow us to see that the established results are tight.
First, we show that if a pair of operators is -monotone for some , then the transformed resolvent is firmly nonexpansive or even contractive. We then show a similar result for the warped resolvent, under some additional assumptions on .
Proposition III.1 (Properties transformed resolvent).
Let , , and suppose is single-valued. If with , then the transformed resolvent has . In particular, if , then the transformed resolvent is firmly nonexpansive. If , then the transformed resolvent is Lipschitz continuous with constant , i.e., contractive.
Proof.
The geometry of this proof is shown in Figure 4.
Since , we find from (2) that . Then, by II.1(ii), we obtain . Further, from the single-valuedness of , II.2(ii) ensures that . Lastly, the property II.2(iii) and the inversion rule II.1(iii) yield that .
Since is the standard SRG of , it follows from [22, Prop. 1 and Thm. 2] that is firmly nonexpansive if and Lipschitz continuous with factor if . ∎
Proposition III.2 (Lipschitz continuity warped resolvent).
Let , , and suppose is single-valued. If with , then the warped resolvent satisfies . If, moreover is well-defined and -Lipschitz, and is -Lipschitz, then .
Proof.
The geometry of the proof is shown in Figure 5.
Remark III.1.
In [16], strong monotonicity of pairs of operators is defined differently from our . Nevertheless, if satisfies [16, Eq. (6)], i.e., , ,
and is -Lipschitz continuous, then and it follows that by Definition II.3, so Proposition III.1 exactly recovers [16, Prop. 3.4].
Example III.1.
Note that the definition of semimonotone operator pairs allows for great flexibility in the choice of . For and , definition II.3 recovers the class of firmly nonexpansive operators:
for all . In that case, the transformed resolvent becomes , i.e., the standard forward step.
Example III.2.
We can recover more relaxed conditions by using the so-called nonlinear preconditioning technique [18, 15, 14, 19]: choosing where is the gradient of a Legendre function [15, p. 6] and . In that case, the semimonotonicity inequality with takes the form
Clearly, this implies that is a monotone operator while if , we recover Example III.1. We remark that by choosing a suitable , we can make the inequality above less restrictive than the one in Example III.1 as shown in [19]. The corresponding transformed resolvent becomes .
Example III.3.
As in example III.1, let . Suppose that , where and is a fixed step size. Then, the transformed resolvent reduces to a nonlinearly preconditioned forward step [19]. Typically, satisfies if and only if , in which case the zeros of correspond to the stationary points of .
Figure 6 visualizes the numerical SRG of the pair for different separable nonlinear preconditioners of the form . Without preconditioning, i.e., for , the pair is clearly not monotone. For hard clipping , and for the numerical SRG suggests monotonicity of the pair .
Lastly, inspired by [16, Ex. 2.3 and 2.4], we provide two more examples to showcase the SRG approach.
Example III.4.
Let . Suppose that , where and is single-valued. If , then .
Proof.
Since , we have that . Then, from the inversion rule II.1(iii), . Further, by II.2(ii), and we conclude that by Proposition II.4. ∎
Example III.5.
Let and suppose is single-valued. If and for some , then .
Proof.
Since and , we have and . Then, by the precomposition rule, II.2(iv), , and from II.1(v) (passing to chord completions if necessary, see [13, Def. 4]), we obtain that , which readily leads to the desired result by Proposition II.4. ∎
Lastly, we provide a result that recovers part of [3, Cor. 25.6] when , and is useful for deriving (linearly) preconditioned algorithms, as we will do in Example IV.2.
Proposition III.3.
Let and suppose is single-valued. Let be a bounded, invertible linear operator on . If , then .
Proof.
Since , we have . Then, from II.2(vii), we obtain and by precomposition, II.2(iv), we have , so we conclude that by Proposition II.4. ∎
IV Application to circuit theory
In this section, we apply the paired monotonicity framework to solve two inclusions involving nonsmooth, multivalued and highly nonmonotone operators in the context of circuit theory. To this end, we first show that linear mappings composed with monotone mappings fit naturally into this framework, a class that includes the NPN transistor. The SRG framework can be used as a simple visual tool for detecting when paired monotonicity fails and may also certify tightness when monotonicity does hold (see Figure 3).
Proposition IV.1.
Let and . Let . Suppose that .
- (i)
If is nonsingular, then for any .
- (ii)
If , then for some .
Proof.
“IV.1(ii)”: By a similar proof as in IV.1(i), provided that , where denotes the adjugate of , i.e., the transpose of the cofactor matrix. To derive this, the defining property of the adjugate is used. Further, if , then and , so admits the factorization for some and the result is proven. See [12, Sec. 0.8.2] for a more detailed discussion on the used properties of adjugates. ∎
Corollary IV.1.
The operator in Example II.2 satisfies , where .
In the following example11 1 The code for reproducing the experiments can be found at https://github.com/alexanderbodard/SRGs_for_pairs., we consider the same experiment as in [20, Prop. 5.1] and provide a transformed proximal point iteration that converges without any step size restriction.
Example IV.1.
We first consider the leaky transistor shown in Figure 7a. The associated inclusion problem is
| (3) |
where and is the ideal diode defined by if ; ; and otherwise. Define . Then, any sequence satisfying the update rule
with as in Corollary IV.1 and step size converges weakly to , where is a solution of (3).
Proof.
By Corollary IV.1, we find that , while per Definition II.3 with , we also obtain that . Similar to Example III.5, we can use II.1(v) to find . Since adding a constant does not change incremental properties, we have . From Proposition III.1, we find that is firmly nonexpansive. It follows from [3, Cor. 5.17] that converges weakly to . ∎
We next show how to leverage Proposition III.3 to derive a linearly preconditioned proximal point algorithm based on the transformed resolvent, before applying this to a (nonmonotone) common-emitter amplifier circuit.
Let and suppose is single-valued with . Let be a bounded, invertible linear operator on . Proposition III.3 ensures that . We can then invoke Proposition III.1 and Krasnosel’skiǐ–Mann [3, Cor. 5.17] to show that the iteration
converges weakly to a point in , provided it exists. Now, define by . With some algebra, the iteration is then equivalent to
Conversely, starting from a positive definite , by letting be the positive definite square root of , we find that the iteration
| (4) |
converges weakly to a point in if one exists. Further, instead of Fejér monotonicity in the Euclidean norm [3, Cor. 5.17(i)], one now obtains Fejér monotonicity in the matrix -norm [12, Eq. (5.2.6)].
We now apply this iteration to a common-emitter amplifier circuit shown in Figure 7b.
Example IV.2.
From [20, Eq. (9)], the behavior of a common-emitter amplifier circuit can be obtained by solving an inclusion problem of the form
| (5) |
where
with and denoting the identity matrix. Such a structure for solving circuits was studied in [6] in the monotone setting and in [20] in the semimonotone setting. We now consider the pair monotonicity setting, that considers parameters that can not be covered by semimonotonicity.
Let be associated with as in Example II.2 and suppose that , and , where denotes the spectral norm of . Consider the iteration
| (6) | ||||
Then, converges weakly to a solution of (5), provided a solution exists.
Proof.
First, note that where the first follows by Corollary IV.1 and the second by the assumption and Definition II.3. Similarly, the skew-symmetric term in (5) can also be shown to be monotone with respect to .
By using the sum rule, II.1(v), and the fact that constant terms do not affect the incremental properties, it follows that the complete operator in (5) is monotone with respect to . Therefore, we can apply (4) with a positive definite . Similar to how Chambolle–Pock [8] arises from a specific choice of in the classical preconditioned proximal point algorithm to decouple the equations [10, Eq. (1.1)], we propose a similar preconditioner , noting that our setting is different due to .
V Conclusion
In this paper, we introduced a novel scaled relative graph for pairs of operators. This framework naturally provides the geometric counterpart for recently introduced assumptions of paired monotonicity of operators. We have shown the practical relevance of these properties by computing the response of two highly nonmonotone circuits, thus extending known theoretical guarantees.
We believe that the proposed scaled relative graph for pairs of operators may prove valuable for stability analysis of feedback systems, and may simplify further analysis of other classes of nonmonotone circuits that can be handled by tailored splitting methods. Other interesting avenues for future work include developing numerical methods for calculating paired SRGs and nonlinear resolvents.
For the proofs, we require the following equivalent formulation of the complex-conjugate pair (see e.g., [22, Eq. (1)]):
| (7) |
where is the projection onto the subspace orthogonal to .
Proof of proposition II.1
Proof.
“II.1(i)”: Denote , which has graph given by . Let . Then there exist and with , hence . Since and , the same point belongs to .
Conversely, if , then for some with . Therefore, there exist such that , . Hence , and the finite parts coincide.
It remains to check the point at infinity. By Definition II.1, if and only if there exist and such that and . Denote and note that , with distinct outputs at the same input , implying . Conversely, if , then there exist with . Thus, there exist such that , . Taking gives , and we conclude that .
“II.1(ii)”: This follows from definition II.1 along with the properties of the inner product and the norm.
“II.1(iii)”: The equality follows for points that are neither nor by definition. If , there exist and such that and . This immediately implies that . The zero case follows similarly.
“II.1(iv)”: The first equality is II.1(iii) and the second is [22, Thm. 5] since is the standard SRG.
Proof of proposition II.2
Proof.
“II.2(i)”: Take . Clearly, since is a function and we would require both and . Since is not constant, there exist with . For any such pair, we have that . Thus .
“II.2(ii)”: Take . Then, there exist and such that . Since , and . Now, implies and , i.e. which then means that . The opposite inclusions follow similarly.
“II.2(iv)”: Take . Then, there exist and such that and . This implies and with . Then, by definition, . Now take . Then, for some pair as before, and meaning also that .
Now assume, moreover, that is surjective. Take . Then, there exist and such that and . Clearly, since is surjective, there exist such that and . Then, and with and thus . The case follows similarly.
“II.2(v)”: Take . If , then there exist and such that and . Therefore, there also exist such that and, since is single-valued and , we have that . This implies , which would contradict the assumption that , and therefore, . Now assume that . This means that with pairs as before such that we have , since is a -Lipschitz operator in light of [22, Prop. 1 and Thm. 2]. This implies that for and thus that . Lastly, from the hypothesis, , so we obtain the claimed result .
“II.2(vi)”: Take . If , then there exist such that and . Since is single-valued, this implies that there exist such that and , i.e., , which contradicts the assumption that , so . In particular, we have that is single-valued.
Further, for , as in the proof of II.2(v), we know that is an -Lipschitz operator. Similarly, from we have that implies and thus that for all . Then, if we have that for some . Using the previous inequality along with the Lipschitz continuity of we obtain , which is the claimed result.
“II.2(vii)”: To begin with, note that since is bounded and invertible, we have that . For any . Thus, for all and such that we have that or, equivalently, by definition of the adjoint [21, Thm. 4.10]. Since is invertible, it holds for any and with that . The case follows similarly and we conclude that . ∎
Proof of Proposition II.3
Proof.
Suppose that there exists a nonnegatively homogeneous function that certifies membership as in Proposition II.3. Notice that by construction, and it remains to show that . For all points and such that , the same technique from [22, Thm. 2] yields that .
Now suppose , while . Then . Thus, there is some such that , , and , . Since , we have that . Multiplying by and using homogeneity, we find . Lastly, if and , then is evaluated at all zeros, and its output must therefore be zero by homogeneity. In all three cases, we have and since the points were arbitrary evaluations of , we have that by the hypothesis.
Conversely, suppose that is SRG-full. Let and consider
where if and if and , and
By our constraint on , will not be evaluated on points where it is not defined. Observe also that as in (7).
Let and . Then also and by definition. It follows that . The cases and both yield , so we obtain that is nonnegatively homogeneous. It remains to show that for all and .
Thus, first assume that . Let and and denote , and . Note that by Cauchy–Schwarz. If , then by construction. Otherwise, if , Definition II.1 and (7) ensure that . Since is SRG-full, it follows that and by construction. Thus, for all points in the graphs.
Second, assume for all and . It suffices to show that from which the desired result follows by SRG-fullness of . To this end, let . Assume . Since the scaled relative graph is symmetric with respect to the real axis, we may assume that . By Definition II.1, there exists some and so that and , where we denoted , and . Further, since is nonnegative by construction, . Therefore, since , we require , implying that by definition of . For , use , so that . Since , , and , we obtain , hence . ∎
Proof of Proposition II.4
Proof.
That is SRG-full follows from Definition II.3 and Proposition II.3 with .
We now show the claimed expression for . Suppose that , and are arbitrary with . It follows from Definition II.3 that , and dividing by yields which is equivalent to , see (7). Now suppose . Clearly, if then also from Definition II.3 and thus .
For the opposite inclusion, suppose . Then the set on the right-hand side is exactly , and from [22, Prop. 1], we find . Otherwise, if , let satisfy . Then . Further, dividing by and completing the square, we obtain
Observe that this defines the same disk as in [20, Prop. 3.4]. Therefore, . The case follows similarly. ∎
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