  The increasing use of machine learning in practice and legal regulations like
EU's GDPR cause the necessity to be able to explain the prediction and behavior
of machine learning models. A prominent example of particularly intuitive
explanations of AI models in the context of decision making are counterfactual
explanations. Yet, it is still an open research problem how to efficiently
compute counterfactual explanations for many models.
  We investigate how to efficiently compute counterfactual explanations for an
important class of models, prototype-based classifiers such as learning vector
quantization models. In particular, we derive specific convex and non-convex
programs depending on the used metric.
