fix chebyshev scalariztaion

Summary: See pytorch#1614

Differential Revision: D42373368

fbshipit-source-id: 0a1417d8865c943851430866df4f9369d0e21bf0

botorch/utils/multi_objective/scalarization.py

@@ -32,13 +32,15 @@ def get_chebyshev_scalarization(
     Augmented Chebyshev scalarization:
         objective(y) = min(w * y) + alpha * sum(w * y)
 
-    Outcomes are first normalized to [0,1] for maximization (or [-1,0] for minimization)
+    Outcomes are multiplied by -1, then normalized to [0,1] for maximization (or [-1,0] for minimization)
     and then an augmented Chebyshev scalarization is applied.
 
+    Since typically the augmented chebyshev scalarization is minimized, we multiply the resulting quantity by -1.
+
     Note: this assumes maximization of the augmented Chebyshev scalarization.
     Minimizing/Maximizing an objective is supported by passing a negative/positive
     weight for that objective. To make all w * y's have positive sign
-    such that they are comparable when computing min(w * y), outcomes of minimization
+    such that they are comparable when computing max(w * y), outcomes of minimization
     objectives are shifted from [0,1] to [-1,0].
 
     See [Knowles2005]_ for details.
@@ -61,6 +63,7 @@ def get_chebyshev_scalarization(
         >>> weights = torch.tensor([0.75, -0.25])
         >>> transform = get_aug_chebyshev_scalarization(weights, Y)
     """
+    Y = -Y
     if weights.shape != Y.shape[-1:]:
         raise BotorchTensorDimensionError(
             "weights must be an `m`-dim tensor where Y is `... x m`."
@@ -71,7 +74,7 @@ def get_chebyshev_scalarization(
 
     def chebyshev_obj(Y: Tensor, X: Optional[Tensor] = None) -> Tensor:
         product = weights * Y
-        return product.min(dim=-1).values + alpha * product.sum(dim=-1)
+        return product.max(dim=-1).values + alpha * product.sum(dim=-1)
 
     if Y.shape[-2] == 0:
         # If there are no observations, we do not need to normalize the objectives
@@ -90,10 +93,10 @@ def chebyshev_obj(Y: Tensor, X: Optional[Tensor] = None) -> Tensor:
 
     def obj(Y: Tensor, X: Optional[Tensor] = None) -> Tensor:
         # scale to [0,1]
-        Y_normalized = normalize(Y, bounds=Y_bounds)
+        Y_normalized = normalize(-Y, bounds=Y_bounds)
         # If minimizing an objective, convert Y_normalized values to [-1,0],
         # such that min(w*y) makes sense, we want all w*y's to be positive
         Y_normalized[..., minimize] = Y_normalized[..., minimize] - 1
-        return chebyshev_obj(Y=Y_normalized)
+        return -chebyshev_obj(Y=Y_normalized)
 
     return obj