Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks.
While adaptive gradient methods theory is well understood for minimization problems, the underlying factors driving their empirical success in min-max problems such as GANs remain unclear.
In this paper, we aim at bridging  this gap from both theoretical and empirical perspectives.
First, we analyze a variant of Optimistic Stochastic Gradient (OSG) proposed in~\citep{daskalakis2017training} for solving a class of non-convex non-concave min-max problem and establish $O(\epsilon^{-4})$ complexity for finding $\epsilon$-first-order stationary point, in which the algorithm only requires invoking one stochastic first-order oracle while enjoying state-of-the-art iteration complexity achieved by stochastic extragradient method by~\citep{iusem2017extragradient}.
Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $\widetilde{O}\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$~\footnote{Here $\widetilde{O}(\cdot)$ compresses a logarithmic factor of $\epsilon$.
}, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$.
To the best of our knowledge, this is the first work for establishing adaptive complexity in non-convex non-concave min-max optimization.
Empirically, our experiments show that indeed adaptive gradient algorithms outperform their non-adaptive counterparts in GAN training.
Moreover, this observation can be explained by the slow growth rate of the cumulative stochastic gradient, as observed empirically.
Adaptive gradient algorithms (Duchi et al., 2011; Tieleman & Hinton, 2012; Kingma & Ba, 2014; Reddi et al., 2019) are very popular in training deep neural networks due to their computational efficiency and minimal need for hyper-parameter tuning (Kingma & Ba, 2014) .
For example, Adagrad (Duchi et al., 2011) automatically adjusts the learning rate for each dimension of the model parameter according to the information of history gradients, while its computational cost is almost the same as Stochastic Gradient Descent (SGD).
However, in supervised deep learning (for example, image classification tasks using a deep convolutional neural network), there is not enough evidence showing that adaptive gradient methods converge faster than its non-adaptive counterpart (i.e., SGD) on benchmark datasets.
For example, it is argued in (Wilson et al., 2017 ) that adaptive gradient methods often find a solution with worse performance than SGD.
Specifically, Wilson et al. (2017) observed that Adagrad has slower convergence than SGD in terms of both training and testing error, while using VGG (Simonyan & Zisserman, 2014) on CIFAR10 data.
GANs (Goodfellow et al., 2014) are a popular class of generative models.
In a nutshell, they consist of a generator and a discriminator, both of which are defined by deep neural networks.
The generator and the discriminator are trained under an adversarial cost, corresponding to a non-convex non-concave min-max problem.
GANs are known to be notoriously difficult to train.
In practice, Adam (Kingma & Ba, 2014 ) is the defacto optimizer used for GAN training.
The common optimization strategy is to alternatively update the discriminator and the generator (Arjovsky et al., 2017; Gulrajani et al., 2017) .
Using Adam is important in GAN training, since replacing it with non-adaptive methods (e.g. SGD) would significantly deteriorate the performance.
This paper studies and attempts to answer the following question:
In this paper, we explain the effectiveness of adaptive gradient methods in training GANs from both theoretical and empirical perspectives.
Theoretically, we provide two efficient stochastic algorithms for solving a class of min-max non-convex non-concave problems with state-of-the-art computational complexities.
We also establish adaptive complexity results for an Adagrad-style algorithm by using coordinate-wise stepsize according to the geometry of the history data.
The algorithm is proven to enjoy faster adaptive convergence than its non-adaptive counterpart when the gradient is sparse, which is similar to Adagrad applied to convex minimization problem.
We have conducted extensive empirical studies to verify our theoretical findings.
In addition, our experimental results suggest that the reason why adaptive gradient methods deliver good practical performance for GAN training is due to the slow growth rate of the cumulative stochastic gradient.
This paper provides novel analysis of adaptive gradient algorithms for solving non-convex non-concave min-max problems as GANs, and explains the reason why adaptive gradient methods outperform its non-adaptive counterparts by empirical studies.