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diff --git a/contrib/lua-torch/optim/adam.lua b/contrib/lua-torch/optim/adam.lua
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+--[[ An implementation of Adam https://arxiv.org/abs/1412.6980
+
+ARGS:
+
+- 'opfunc' : a function that takes a single input (X), the point
+             of a evaluation, and returns f(X) and df/dX
+- 'x'      : the initial point
+- 'config` : a table with configuration parameters for the optimizer
+- 'config.learningRate'      : learning rate
+- `config.learningRateDecay` : learning rate decay
+- 'config.beta1'             : first moment coefficient
+- 'config.beta2'             : second moment coefficient
+- 'config.epsilon'           : for numerical stability
+- 'config.weightDecay'       : weight decay
+- 'state'                    : a table describing the state of the optimizer; after each
+                              call the state is modified
+
+RETURN:
+- `x`     : the new x vector
+- `f(x)`  : the function, evaluated before the update
+
+]]
+
+function optim.adam(opfunc, x, config, state)
+   -- (0) get/update state
+   local config = config or {}
+   local state = state or config
+   local lr = config.learningRate or 0.001
+   local lrd = config.learningRateDecay or 0
+
+   local beta1 = config.beta1 or 0.9
+   local beta2 = config.beta2 or 0.999
+   local epsilon = config.epsilon or 1e-8
+   local wd = config.weightDecay or 0
+
+   -- (1) evaluate f(x) and df/dx
+   local fx, dfdx = opfunc(x)
+
+   -- (2) weight decay
+   if wd ~= 0 then
+      dfdx:add(wd, x)
+   end
+
+   -- Initialization
+   state.t = state.t or 0
+   -- Exponential moving average of gradient values
+   state.m = state.m or x.new(dfdx:size()):zero()
+   -- Exponential moving average of squared gradient values
+   state.v = state.v or x.new(dfdx:size()):zero()
+   -- A tmp tensor to hold the sqrt(v) + epsilon
+   state.denom = state.denom or x.new(dfdx:size()):zero()
+
+   -- (3) learning rate decay (annealing)
+   local clr = lr / (1 + state.t*lrd)
+
+   state.t = state.t + 1
+
+   -- Decay the first and second moment running average coefficient
+   state.m:mul(beta1):add(1-beta1, dfdx)
+   state.v:mul(beta2):addcmul(1-beta2, dfdx, dfdx)
+
+   state.denom:copy(state.v):sqrt():add(epsilon)
+
+   local biasCorrection1 = 1 - beta1^state.t
+   local biasCorrection2 = 1 - beta2^state.t
+   local stepSize = clr * math.sqrt(biasCorrection2)/biasCorrection1
+   -- (4) update x
+   x:addcdiv(-stepSize, state.m, state.denom)
+
+   -- return x*, f(x) before optimization
+   return x, {fx}
+end