BibTeX
@ARTICLE{
         Griewank1992ALG,
       ad_theotech = "Adjoint, Checkpointing, Data Flow Analysis, General, Introduction, Reverse
         Mode",
       author = "Andreas Griewank",
       journal = "Optimization Methods and Software",
       pages = "35--54",
       title = "Achieving Logarithmic Growth of Temporal and Spatial Complexity in Reverse Automatic
         Differentiation",
       volume = "1",
       year = "1992",
       abstract = "In its basic form the reverse mode of automatic differentiation yields gradient
         vectors at a small multiple of the computational work needed to evaluate the underlying scalar
         function. The practical applicability of this temporal complexity result, due originally to
         Linnainmaa, seemed to be severely limited by the fact that the memory requirement of the basic
         implementation is proportional to the run time $T$, of the original evaluation program. It is shown
         here that, by a recursive scheme related to the multilevel differentiation approach of Volin and
         Ostrovskii, the growth in both temporal and spatial complexity can be limited to a fixed multiple of
         $\log(T)$. Other compromises between the run time and memory requirement are possible, so that
         the reverse mode becomes applicable to computational problems of virtually any size."
}