# Constructing a Maximum Entropy Classifier for Sentence Extraction

So I'm reading this paper which uses a max ent classifier for sentence extraction. The parametric form for a conditional max ent model is :

$$P(c|s) = \frac{1}{Z(s)} \exp \sum_i \lambda_if_i(c,s)$$

where $f_i(c,s)$ is a feature which has a weight $\lambda_i$ associated with it.

Now, the paper states that conjugate descent was used to find the optimal set of weights (page 3) - and this is what I'm unable to comprehend. How to use/apply conjugate gradient descent to calculate the optimal set of weights?