Paper: A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization (ICLR-2024)

Reviewer comment (Theory):
In the proposed method, an additional optimization problem is required to solve every iteration, i.e., Eq. (11). Thus the proposed method seems inefficient since it is a nested-loop algorithm.

Author response:
**A1**: (1) As discussed in the paragraph after problem (11), problem (11) is a quadratic programming problem and its first term of the objective function can be simplified to $R^\top \Lambda^\top \Lambda R$, where $R = (\lambda_1, \ldots , \lambda_m, \gamma)^\top$ and $\Lambda = (\nabla F_1, \ldots ,\nabla F_m,\nabla \widetilde{q})$. Note that the dimension of the matrix $\Lambda^\top \Lambda$ is $(m+1)\times(m+1)$, which is independent with the dimension of $z$. As the number of UL objectives $m$ is usually small, problem (11) can be solved efficiently. 

(2) We have provided the complexity analysis for the proposed FORUM method and two MOBLO baselines (i.e., MOML and MoCo) in Section 4.1, and showed their running time in Figure 1. According to the results, FORUM is more efficient than MOML and MoCo.

Reviewer comment (Novelty):
I suggest the authors add a table to compare the differences between the proposed methods and existing MOBLO methods (i.e., MOML and MoCo) to clearly show the advantages of the proposed method.

Author response:
**A2**: Thanks for your suggestion. In the following table where $n$, $p$, $m$ and $T$ denote the dimension of variable $\alpha$, the dimension of variable $\omega$, the number of objectives, and the number of LL iterations, respectively, we show the differences between the proposed methods and other gradient-based MOBLO methods (i.e. MOML and MoCo). 

\begin{array}{cccc}
    \hline
    \text{Method} & \text{Convergence analysis} & \text{Computational cost} & \text{Space cost} \newline
    \hline
    \text{MOML} & \text{asymptotic} & \mathcal{O}(mp(n+p)T) &  \mathcal{O}(mn+mpT)\newline
    \text{MoCo} & \text{non-asymptotic}  & \mathcal{O}(mp(n+p)T) & \mathcal{O}(2mn+mpT)\newline
    \text{FORUM} & \text{non-asymptotic} & \mathcal{O}(mn+p(m+T)) & \mathcal{O}(mn+mp)\newline
    \hline
\end{array}

The main advantage of our method is that it has lower computational and memory costs. Our method also has better experimental results in practical problems, as observed in our experiments on multi-objective data hyper-cleaning and multi-task learning settings. 

We have added this table in Section F of the updated paper.

Reviewer comment (Novelty):
The proposed algorithm is a straightforward combination of two existing methods, and the accompanying analysis appears rather standard. Consequently, the technical innovation compared to prior work upon which this study is based is limited.

Author response:
The FORUM method is a novel approach for effectively solving the challenging Multi-Objective Bi-Level Optimization (MOBLO) problem. Unlike previous value-function-based approaches, our method specifically addresses the MOBLO problem for the first time. We further propose a novel max-min optimization problem to determine the aggregated weights and provide an update scheme with theoretical convergence analysis. Therefore, the proposed algorithm is not a straightforward combination of existing methods.

Reviewer comment (Theory):
The convergence result of the proposed method lacks persuasiveness. As pointed out by the authors, the constraint $q(z) \le 0$ in the reformulated problem (3) is ill-posed, rendering the KKT stationary condition not a necessary condition for problem (3) solutions. Therefore, the utilization of the $\mathcal{K}(z)$ measure for the convergence of the proposed method in the main convergence theorem (Theorem 4.3) is inappropriate. In contrast, the MoCo method by Fernando et al. (2023) employs hyper-gradients under the same strong convexity in the LL problem to characterize convergence and establish convergence to Pareto stationarity.

Author response:
The convergence of the proposed method can be measured by $\mathcal{K}(z)$ and $q(z)$. For a constrained optimization problem with a specific structure (i.e., $q(z)\le 0$ does not have an interior point), the convergence can be measured by the problem's stationarity condition and feasibility condition. This measure of convergence has been used for the value-function-based bi-level optimization method [1]. In our case, the condition $\sum_{i=1}^{m}\lambda_i \nabla F_i(z^*) + \nu \nabla q(z^*) =0$ indicates the local optimality of $F(z)$ as it is the KKT stationarity condition for the constrained multi-objective optimization problem. When $q(z^*)=0$, it indicates that $\omega^*$ is a minimum point of $f$, i.e., $\omega^* = \text{argmin}_{\omega} f(\alpha,\omega)$, ensuring the satisfaction of the feasibility condition $q(z^*)\le 0$. Therefore, $\mathcal{K}(z)$ and $q(z)$ can be used to measure the convergence of problem (3) and the original MOBLO problem, since these two problems are equivalent. 

MoCo uses a different reformulation strategy to simplify the original MOBLO problem into an unconstrained MOO problem. Therefore, they can measure the convergence by the Pareto stationarity. However, this approach employs hyper-gradients, which are computationally inefficient, as evidenced by the complexity analysis in Section 4.1 and experimental results in Figure 1. In our work, we focus on studying an efficient method to solve the MOBLO problem, so we do not apply their reformulation method.

Reviewer comment (Theory):
The analysis of the proposed algorithm is confined to a deterministic setting, which may restrict its applicability given that the motivating applications are mostly in the stochastic setting.

Author response:
Though the proposed method and its convergence analysis are based on the deterministic gradient descent, our experimental results demonstrate it is also suitable for stochastic gradient descent in practice and achieves state-of-the-art performance on two learning problems, i.e., data hyper-cleaning and multi-task learning.

Reviewer comment (Theory):
The strong convexity of the LL problem appears to play a pivotal role in the convergence analysis, which, in turn, constrains the applicability of the proposed method. Notably, the MOML method introduced by Ye et al. (2022) does not necessitate such an assumption.

Author response:
MOML does not need such an assumption since it only provides the asymptotic convergence analysis, i.e., the Pareto optimal set of $F(\alpha,\omega^T(\alpha))$ will converge to the set of $F(\alpha,\omega^*(\alpha))$. However, in this paper, we provide the non-asymptotic convergence analysis, where we need to reach the solution of the Pareto optimal set of $F(\alpha,\omega^T(\alpha))$, thus, the strong convexity assumption of the LL problem is needed. Similarly, MoCo also needs the strong convexity assumption of the LL problem for the non-asymptotic convergence analysis.

Reviewer comment (Theory):
However, I have quite a few concerns regarding the analysis and the novelty. First, to deal with the reformulated constraint in the value-function based method, the authors use a dot product between $d$ and the gradient of the constraint and make it less or equal to $-\phi_k$. However, how to pick this $\phi_k$ in practice and in theory? Also, the final convergence criterion seems questionable. For example, the authors use the measure of KKT stationary condition to as convergence criterion. How does this condition correlate with Parato stationarity? How fast does the parameter $v_k$ decrease to 0 in this criterion? All these questions are not well explained in this work.

Author response:
**A1**: (i) We have discussed how to pick this $\phi_k$ in Section 3.2 in the submission. We set $\phi_k=\frac{\rho}{2}||\nabla \widetilde{q}(z_k)||^2$ in practice and in theoretical analysis, where $\rho$ is a positive constant. 
Then when $\phi_k>0$, it means that $||\nabla \tilde{q}(z)||\not=0$ and $\widetilde{q}(z)$ should be further optimized, and $\langle \nabla \widetilde{q}(z_k),-d\rangle \ge \phi_k > 0$ can enforce $\widetilde{q}(z)$ to decrease. When $\phi_k$ equals $0$, it indicates that the optimum of $\widetilde{q}(z)$ is reached and $\langle \nabla \widetilde{q}(z_k),-d\rangle \ge \phi_k = 0$ also holds. Thus, the dynamic $\phi_k$ can ensure $d_k$ to iteratively decrease $\widetilde{q}(z)$ such that the constraint $\widetilde{q}(z)\le0$ is satisfied. 

(ii) It is worth noting that the FORUM algorithm is proposed for MOBLO rather than MOO. The Pareto stationary is a first-order KKT stationary condition for the **unconstrained** MOO optimization problem. However, we reformulate MOBLO to an equivalent **constrained** MOO problem. Hence, the Pareto stationary cannot be used as a convergence criterion in our method. We measure the convergence by the local optimality condition of the constrained MOO problem, i.e., KKT stationary and feasibility conditions. 

We have added this discussion in Section B.1 of the updated paper.

(iii) The KKT stationary condition only requires $\nu_k \ge 0$ and $\mathcal{K}(z_k) =0$. Since $\nabla q(z_k)$ will converge to 0 as $q(z_k)\to 0$, $\nu_k$ is not necessary to decrease to 0.

Reviewer comment (Theory):
The algorithm has some unclear parts. For example, the authors use the idea of momentum update on $\lambda$ update. This step is originally proposed in Zhou et al., 2022 (the authors should mention about this). However, there is characterization on the distance between the true variable $\lambda_k$ and the surrogate $\titilde \lambda_k$? This is important, because what you need to use is $\lambda_k$ rather than surrogate $\titilde \lambda_k$ in the algorithm.

Author response:
**A2**: Thanks for your suggestion. We have mentioned this in Section 3.2 in the updated paper. The distance between $\lambda^k$ and $\tilde{\lambda}^k$  can be expressed as $||\lambda^k- \tilde{\lambda}^k|| = (1-\beta_k) ||\lambda^k- \tilde{\lambda}^{k-1} ||$. As a result, the distance is upper-bounded by $\sqrt{2}$. In the initial stages, when $k$ is small, $\beta_k$ approximates $1$, leading to a small distance. This allows the algorithm to retain the advantage of maximizing the minimum decrease across all upper-level objectives. As $\beta_k \to 0$ and $\tilde{\lambda}^k$ converge to a limit point, the distance increases. So in the later stage, the FORUM method performs similarly to the fixed linear scalarization for the UL subproblem, ensuring convergence.

Reviewer comment (Theory):
The algorithm is deterministic without data sampling, but in the experiments it seems data sampling is used. I am wondering if it is possible to extend the algorithm and analysis to the more practice stochastic setting? If not, what are the challenges? Some recent progresses on stochastic MOO may be helpful here (some of them are missing in this work). 

[1] Suyun Liu and Luis Nunes Vicente. The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning. 

[2] Lisha Chen, Heshan Fernando, Yiming Ying, and Tianyi Chen. Three-way trade-off in multi-objective learning: Optimization, generalization and conflict-avoidance.

[3] Heshan Devaka Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, and Tianyi Chen. Mitigating gradient bias in multi-objective learning: A provably convergent approach.

[4] Peiyao Xiao, Hao Ban, and Kaiyi Ji. Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms.

Author response:
**A3**: (i) Extending the analysis of our algorithm to the stochastic setting might be challenging. We think there are two main technical difficulties. Firstly, for stochastic gradient descent, the lower-level optimization can not maintain an exponential convergence rate, so Lemma B.1. (c) no longer holds. This will affect our convergence proof. Secondly, the stochastic setting introduces an approximation error term $||\nabla_z q(z) - \nabla_z q(z;\xi)||$, where $\nabla_z q(z;\xi)$ represents the stochastic gradient. Though we can make a boundedness assumption for this error, it will still introduce an additional constant term for our current boundedness result such as Lemma B.2. (b), and it is hard to cancel this term for our nested algorithm. It is interesting to analyze the effect of the stochastic gradient approximation on our algorithm theoretically, and we regarded it as a future work.

(ii) Though the proposed method and its convergence analysis are based on deterministic gradient descent, our experimental results demonstrate its suitability for stochastic gradient descent in practice. Our method achieves state-of-the-art performance on two learning problems, i.e., data hyper-cleaning and multi-task learning.

Reviewer comment (Theory):
The analysis assumes the upper-level function $F_i$ is bounded. However, this has not been made in the aforementioned [1,2,3,4] works. More clarifications should be provided. If there are any special challenges making this assumption necessary? Also, the assumption $q(z_k)<B$ is a strong assumption that has not been made in previous bilevel and MOO literatures, because bounded function value and strong-convexity cannot be made simultaneously.

Author response:
**A4**: (i) To prove the convergence of gradient norm $||d_k||^2$, we sum the inequality of $||d_k||^2$ over $k = 0, 1,...,K-1$ and obtain the inequality (22) in Appendix. This inequality leads to an upper bounded term $\sum_{k=0}^{K-1}\sum_{i=1}^m(\tilde{\lambda}^k_i -\tilde{\lambda}^{k+1}_i)F(z^{k+1})$. Though the convergence of $\tilde{\lambda}^k_i -\tilde{\lambda}^{k+1}_i$ can be controlled by the momentum parameter $\beta_k$, $F_i(z)$ should be bounded to make the whole term decrease to zero. Therefore, for our method, the boundedness assumption of the upper-level function $F_i$ is necessary.

Since the objective functions are assumed to be convex or strongly convex in [1], the boundedness assumption can be unnecessary in [1]. However, our convergence analysis is based on the non-convex case. The MGDA-based methods [2,3,4] all apply a gradient descent method to update the weights $\lambda$ in each iteration. For solving a standard MOO problem $F(x)$, these methods have technical advantages, as the convergence can be controlled by the learning rate of $\lambda$ and the form of the gradient of $\lambda$ is very simple, i.e., $\nabla F^T \nabla F$ ([2,3] add an additional regularization term, so their corresponding gradient should add a positive constant). Overall, this updated approach of $\lambda$ benefits their convergence analysis and allows them to avoid boundedness assumptions. In contrast, our work focuses on solving an MOBLO problem rather than MOO. The optimization problem of $\lambda$ is problem (10), which is different from that of the MGDA-based MOO methods [2,3,4]. For this optimization problem (10), the expression of the gradient of $\lambda$ is very complex, since we have an additional $\nu\nabla q(z)$ term and there exists a max operator in the objective function. So it is hard to use the strategies in [2,3,4] to remove the boundedness assumption in our method.

Moreover, in the context of non-convex optimization, the boundedness assumption on the objective function value is still commonly employed in both multi-objective [3,5] and single-objective optimization [6] problems. Therefore, we employ this assumption to facilitate the convergence analysis of the proposed FORUM method in addressing the MOBLO problem with a non-convex UL subproblem.

(ii) This boundedness assumption $q(z_k)\le B$ is only made for the generated sequence $z_k$ rather than for the entire $\mathbb{R}^{n+p}$ space in our non-asymptotic analysis. So it can hold with strong convexity of function $f$. When we conduct our algorithm on a local bounded region of $z$ for a Lipschitz continuous function $f$, this assumption can also hold. Therefore it is not a very strong assumption.

Reviewer comment (Experiments):
Limited scope: The paper only evaluates the proposed FORUM algorithm on two learning problems, i.e., multi-objective data hyper-cleaning and multi-task learning on three benchmark datasets. The generalizability of the proposed method to other learning problems is not thoroughly explored.

Author response:
**A1**: (1) We evaluate the proposed FORUM method on **two** datasets for multi-objective data hyper-cleaning and **three** datasets for multi-task learning, which includes image classification, dense prediction, and molecular property prediction. Extensive experimental results demonstrate FORUM achieves state-of-the-art performance on all datasets.

(2) FORUM is a general algorithm that can be applied to any learning problems taking the optimization form of a multi-objective bi-level optimization problem. We will study more learning problems in the future.

Reviewer comment (Reproducibility):
Lack of implementation details: The paper does not provide detailed implementation information about the proposed FORUM algorithm, such as the specific hyperparameters used in the experiments. This makes it difficult for other researchers to reproduce the results and compare the proposed method with their own algorithms.

Author response:
**A3**: The implementation details, including specific hyperparameters, are provided in Appendix D: Appendix D.1 for multi-objective data hyper-cleaning (Section 5.1) and Appendix D.2 for multi-task learning (Section 5.2). We will release the code later.

Reviewer comment (Theory):
Regarding the use of approximation methods in the paper, while the paper proposes an approximation method to compute ω∗(α) and approximates the constraint function q(z) using eq(z) = f(z)−f(α, ˜ωT ), it is not clear how the approximation errors affect the performance of the proposed FORUM algorithm. There is a need for more insights into the impact of the approximation errors on the convergence and efficiency of the proposed method.

Author response:
**A4**: (i) We have analyzed the error bound of this approximation in Lemma A.1 in the submission. The approximation error exponential decay w.r.t. the lower-level iterations $T$. 

(ii) We have also analyzed the influence of this approximation on the convergence result, as shown in Theorem 4.3 in the submission. The convergence rate of FORUM is of the order $\mathcal{O}(K^{-\frac{1}{4}}+\Gamma(T))$, where $\Gamma(T)$ represents exponential decays w.r.t $T $. Therefore, as $T\to +\infty$, the influence of the approximation error on the convergence rate decreases, and the algorithm converges faster.

(iii) We have conducted experiments to show the effect of the approximation error (i.e., with different $T$), and the results are reported in Table 1 in the submission. The results show that the performance is not so sensitive over a large range of $T$.