31. The transformation that gives the minimal cost function is chosen as the model for head motion. 32. The cost function can be much more complicated. 33. Having established the cost function , the algorithm simply uses gradient descent to find the optimal transformation. 34. These viable solutions are judged by their satisfaction of a series of measures or cost functions . 35. These are generally referred to as cost functions and the other measures are treated as constraints. 36. This algorithm calculates the shortest path using the number of optical routers as the cost function . 37. Concave cost functions represent this economy of scale. 38. The adaptation step of the neural gas can be interpreted as gradient descent on a cost function . 39. At the long end, a regression technique with a cost function that values smoothness might be used. 40. This algorithm calculates the p shortest paths using the number of optical routers as the cost function .
