You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: creases with the position of the layer in the network. Hence a given input image (cid:126)x is encoded in each layer of the CNN by the ﬁlter responses to that image. A layer with N l distinct ﬁlters has N l feature maps each of size M l , where M l is the height times the width of the feature map. So the responses in a layer l can be stored in a matrix F l ∈ R N l × M l where F lij is the activation of the i th ﬁlter at position j in layer l . To visualise the image information that is encoded at different layers of the hierarchy (Fig 1, content reconstructions) we perform gradient descent on a white noise image to ﬁnd another image that matches the feature responses of the original image. So let (cid:126)p and (cid:126)x be the original image and the image that is generated and P l and F l their respective feature representation in layer l . We then deﬁne the squared-error loss between the two feature representations To generate a texture that matches the style of a given image (Fig 1, style reconstructions), we use gradient descent from a white noise image to ﬁnd another image that matches the style representation of the original image. This is done by minimising the mean-squared distance between the entries of the Gram matrix from the original image and the Gram matrix of the image to be generated. So let (cid:126)a and (cid:126)x be the original image and the image that is generated and A l and G l their respective style representations in layer l . The contribution of that layer to the total loss is then The gradients of E l with respect to the activations in lower layers of the network can be readily computed using standard error back-propagation. The ﬁve style reconstructions in Fig 1 were generated by matching the style representations on layer ‘conv1 1’ (a), ‘conv1 1’ and ‘conv2 1’ (b), ‘conv1 1’, ‘conv2 1’ and ‘conv3 1’ (c), ‘conv1 1’, ‘conv2 1’, ‘conv3 1’ and ‘conv4 1’ (d), ‘conv1 1’, ‘conv2 1’, ‘conv3 1’, ‘conv4 1’ and ‘conv5 1’ (e). To generate the images that mix the content of a photograph with the style of a painting (Fig 2) we jointly minimise the distance of a white noise image from the content representation 11

Question: Why did the authors particularly use "Gradient Descent" instead of any other optimization algorithm?
The work uses gradient descent to transform the white noise image to match the stylistic and content representations of an artwork and a photograph respectively. However, they do not discuss the reasons behind choosing gradient descent over other methods and do not provide alternatives.