Still getting different ouputs for different executions of arbitrage.mpc

arbitrage.mpc

@@ -10,6 +10,8 @@ sfix.set_precision(f=16, k=40)
 program.use_edabit(True)
 program.use_split(4)
 
+program.use_trunc_pr = False
+
 # Create pools
 #reserves_eq = sfix.Array(2)
 reserves_eq = Matrix(2, 1, sfix)
@@ -49,6 +51,7 @@ net_trade = router.netflows()
 price_vector_tmp = Matrix(1, 2, sfix)
 price_vector_tmp.assign(router.v)
 print_ln("%s", router.v.reveal())
+net_trade.print_reveal_nested()
 print_ln("Profit: %s", price_vector_tmp.dot(net_trade).reveal())
 #print_ln_to(0, "Net trade: ")
 #net_trade.print_reveal_nested()

cfmms.mpc

@@ -3,13 +3,14 @@
 """
 
 from Compiler import mpc_math
-
+"""
 sfix.round_nearest = True
 cfix.set_precision(f=16, k=40)
 sfix.set_precision(f=16, k=40)
 
 program.use_edabit(True)
 program.use_split(4)
+"""
 
 class CFMM:
  """

optimizers.mpc

@@ -1,14 +1,15 @@
 from Compiler import mpc_math, ml
-
+sfix.round_nearest = True
+"""
 program.use_edabit(True)
 program.use_split(4)
+"""
 
-
-sfix.round_nearest = True
 cfix.set_precision(f=16, k=40)
 sfix.set_precision(f=16, k=40)
-
+"""
 program.use_edabit(True)
+"""
 
 class Optimizer:
 
@@ -34,7 +35,7 @@ class SGDOptimizer(Optimizer):
 
  def optimize(self, initial_guess, learning_rate=sfix(0.01), n_iter=10):
  guess = sfix(initial_guess)
- @for_range(n_iter)
+ @for_range_opt(n_iter)
  def _(i):
  guess.update(guess - learning_rate * self.function.grad(guess))
 
@@ -85,15 +86,13 @@ class L_BFGS_BOptimizer(Optimizer):
  def _():
  break_loop()
 
- def optimize(self, initial_guess, fn=None, g=None, memory_size=17, threshold=cfix(0.0001), alpha=MemValue(cfix(0.5)), rho=cfix(0.5), armijo_c=cfix(0.0001), c_2=cfix(0.9), n_iter=10):
+ def optimize(self, initial_guess, fn=None, g=None, threshold=cfix(0.0001), alpha=MemValue(cfix(0.5)), rho=cfix(0.5), armijo_c=cfix(0.0001), c_2=cfix(0.9), n_iter=10):
  k = regint(0)
 
  guess_k = Matrix(len(initial_guess), 1, sfix)
- #guess_k = Array.create_from(initial_guess)
  guess_k.assign(initial_guess)
- #print("from optimizers.mpc")
- #print(type(initial_guess))
- grad_f_k = g(guess_k)
+ grad_f_k = Matrix(len(initial_guess), 1, sfix)
+ grad_f_k.assign(g(guess_k))
  prev_guess_k = Matrix(len(initial_guess), 1, sfix)
  prev_grad_k = Matrix(len(initial_guess), 1, sfix)
 
@@ -103,8 +102,6 @@ class L_BFGS_BOptimizer(Optimizer):
 
  @for_range_opt(n_iter)
  def _(i):
- #print_ln("%s", self.function.grad(guess_k).reveal())
- #print_ln("%s", threshold)
  @if_e((self.function.norm(g(guess_k)) > threshold).reveal())
  def _():
  #print_ln("%s", (self.function.norm(self.function.grad(guess_k)) > threshold).reveal())
@@ -117,12 +114,6 @@ class L_BFGS_BOptimizer(Optimizer):
  tmp_grad_f_k = Matrix(len(grad_f_k), 1, sfix)
  tmp_grad_f_k.assign(grad_f_k)
  p_vec_k.assign(invH.dot(tmp_grad_f_k))
- #print_ln("%s", invH.dot(grad_f_k).reveal())
- print_ln("%s", p_vec_k.reveal())
- #invH.dot(grad_f_k).print_reveal_nested()
-
- # Compute line search using backtracking
- # It's okay to reveal here since we aren't revealing guess_k itself
 
  self.update_alpha(guess_k, p_vec_k, rho, alpha, armijo_c, c_2, n_iter=10, fn=fn, g=g)
 

router.mpc

@@ -1,12 +1,14 @@
 exec(open("../cfmms.mpc").read())
 exec(open("../optimizers.mpc").read())
 
+"""
 sfix.round_nearest = True
 cfix.set_precision(f=16, k=40)
 sfix.set_precision(f=16, k=40)
 
 program.use_edabit(True)
 program.use_split(4)
+"""
 
 class ArbitrageRouter:
 
@@ -51,7 +53,7 @@ class ArbitrageRouter:
  #NB: Maybe there might be a way to leverage this to make the computation faster. To be explored later. 
  def fn(v):
  tmp = Matrix(len(self.v), 1, sfix)
- @for_range(len(self.v))
+ @for_range_opt(len(self.v))
  def _(i):
  tmp[i].assign(((v[i][0] <= self.v[i][0]).if_else(0, 1)))
 
@@ -80,7 +82,7 @@ class ArbitrageRouter:
 
  tmp = Matrix(len(self.v), 1, sfix)
  #tmp = []
- @for_range(len(self.v))
+ @for_range_opt(len(self.v))
  def _(i):
  tmp[i].assign(((v[i][0] <= self.v[i][0]).if_else(0, 1)))
 
@@ -111,7 +113,7 @@ class ArbitrageRouter:
 
 
  self.find_arb(self.v)
- new_price_vector = optimizer.optimize(self.v, fn, g, threshold=cfix(1))[0]
+ new_price_vector = optimizer.optimize(self.v, fn, g)[0]
  #print_ln("router.v: %s", new_price_vector.reveal())
  self.v.assign(new_price_vector)
  #print("from router.route")