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diff --git a/contrib/lua-torch/optim/nag.lua b/contrib/lua-torch/optim/nag.lua
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+----------------------------------------------------------------------
+-- An implementation of SGD adapted with features of Nesterov's
+-- Accelerated Gradient method, based on the paper
+-- On the Importance of Initialization and Momentum in Deep Learning
+-- Sutsveker et. al., ICML 2013
+--
+-- ARGS:
+-- opfunc : a function that takes a single input (X), the point of
+--          evaluation, and returns f(X) and df/dX
+-- x      : the initial point
+-- state  : a table describing the state of the optimizer; after each
+--          call the state is modified
+--   state.learningRate      : learning rate
+--   state.learningRateDecay : learning rate decay
+--   state.weightDecay       : weight decay
+--   state.momentum          : momentum
+--   state.learningRates     : vector of individual learning rates
+--
+-- RETURN:
+-- x     : the new x vector
+-- f(x)  : the function, evaluated before the update
+--
+-- (Dilip Krishnan, 2013)
+--
+
+function optim.nag(opfunc, x, config, state)
+   -- (0) get/update state
+   local config = config or {}
+   local state = state or config
+   local lr = config.learningRate or 1e-3
+   local lrd = config.learningRateDecay or 0
+   local wd = config.weightDecay or 0
+   local mom = config.momentum or 0.9
+   local damp = config.dampening or mom
+   local lrs = config.learningRates
+   state.evalCounter = state.evalCounter or 0
+   local nevals = state.evalCounter
+
+   if mom <= 0 then
+     error('Momentum must be positive for Nesterov Accelerated Gradient')
+   end
+
+   -- (1) evaluate f(x) and df/dx
+   -- first step in the direction of the momentum vector
+
+   if state.dfdx then
+      x:add(mom, state.dfdx)
+   end
+   -- then compute gradient at that point
+   -- comment out the above line to get the original SGD
+   local fx,dfdx = opfunc(x)
+
+   -- (2) weight decay
+   if wd ~= 0 then
+      dfdx:add(wd, x)
+   end
+
+   -- (3) learning rate decay (annealing)
+   local clr = lr / (1 + nevals*lrd)
+
+   -- (4) apply momentum
+   if not state.dfdx then
+      state.dfdx = torch.Tensor():typeAs(dfdx):resizeAs(dfdx):fill(0)
+   else
+      state.dfdx:mul(mom)
+   end
+
+   -- (5) parameter update with single or individual learning rates
+   if lrs then
+      if not state.deltaParameters then
+         state.deltaParameters = torch.Tensor():typeAs(x):resizeAs(dfdx)
+      end
+      state.deltaParameters:copy(lrs):cmul(dfdx)
+      x:add(-clr, state.deltaParameters)
+      state.dfdx:add(-clr, state.deltaParameters)
+   else
+      x:add(-clr, dfdx)
+      state.dfdx:add(-clr, dfdx)
+   end
+
+   -- (6) update evaluation counter
+   state.evalCounter = state.evalCounter + 1
+
+   -- return x, f(x) before optimization
+   return x,{fx}
+end