fea: add extra args to optimize_acqf_homotopy

botorch/optim/optimize.py

@@ -489,7 +489,8 @@ def optimize_acqf(
             functions and `gen_batch_initial_conditions` otherwise. Must be specified
             for nonlinear inequality constraints.
         timeout_sec: Max amount of time optimization can run for.
-        return_full_tree:
+        return_full_tree: Return the full tree of optimizers of the previous
+            iteration.
         retry_on_optimization_warning: Whether to retry candidate generation with a new
             set of initial conditions when it fails with an `OptimizationWarning`.
         ic_gen_kwargs: Additional keyword arguments passed to function specified by
@@ -623,7 +624,8 @@ def optimize_acqf_cyclic(
             functions and `gen_batch_initial_conditions` otherwise. Must be specified
             for nonlinear inequality constraints.
         timeout_sec: Max amount of time optimization can run for.
-        return_full_tree:
+        return_full_tree: Return the full tree of optimizers of the previous
+            iteration.
         retry_on_optimization_warning: Whether to retry candidate generation with a new
             set of initial conditions when it fails with an `OptimizationWarning`.
         ic_gen_kwargs: Additional keyword arguments passed to function specified by

botorch/optim/optimize_homotopy.py

@@ -3,14 +3,23 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from collections.abc import Callable
+from __future__ import annotations
+
+from typing import Any
 
 import torch
 from botorch.acquisition import AcquisitionFunction
 from botorch.optim.homotopy import Homotopy
 from botorch.optim.optimize import optimize_acqf
 from torch import Tensor
 
+from collections.abc import Callable
+
+from botorch.generation.gen import TGenCandidates
+from botorch.optim.initializers import (
+    TGenInitialConditions,
+)
+
 
 def prune_candidates(
     candidates: Tensor, acq_values: Tensor, prune_tolerance: float
@@ -50,37 +59,120 @@ def optimize_acqf_homotopy(
     acq_function: AcquisitionFunction,
     bounds: Tensor,
     q: int,
-    homotopy: Homotopy,
     num_restarts: int,
+    homotopy: Homotopy,
+    prune_tolerance: float = 1e-4,
     raw_samples: int | None = None,
-    fixed_features: dict[int, float] | None = None,
     options: dict[str, bool | float | int | str] | None = None,
     final_options: dict[str, bool | float | int | str] | None = None,
-    batch_initial_conditions: Tensor | None = None,
+    inequality_constraints: list[tuple[Tensor, Tensor, float]] | None = None,
+    equality_constraints: list[tuple[Tensor, Tensor, float]] | None = None,
+    nonlinear_inequality_constraints: list[tuple[Callable, bool]] | None = None,
+    fixed_features: dict[int, float] | None = None,
     post_processing_func: Callable[[Tensor], Tensor] | None = None,
-    prune_tolerance: float = 1e-4,
+    batch_initial_conditions: Tensor | None = None,
+    gen_candidates: TGenCandidates | None = None,
+    sequential: bool = False,
+    *,
+    ic_generator: TGenInitialConditions | None = None,
+    timeout_sec: float | None = None,
+    return_full_tree: bool = False,
+    retry_on_optimization_warning: bool = True,
+    **ic_gen_kwargs: Any,
 ) -> tuple[Tensor, Tensor]:
     r"""Generate a set of candidates via multi-start optimization.
 
     Args:
         acq_function: An AcquisitionFunction.
-        bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
+        bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`
+            (if inequality_constraints is provided, these bounds can be -inf and
+            +inf, respectively).
         q: The number of candidates.
         homotopy: Homotopy object that will make the necessary modifications to the
             problem when calling `step()`.
+        prune_tolerance: The minimum distance to prune candidates.
         num_restarts: The number of starting points for multistart acquisition
             function optimization.
         raw_samples: The number of samples for initialization. This is required
             if `batch_initial_conditions` is not specified.
+        options: Options for candidate generation in the initial step of the homotopy.
+        final_options: Options for candidate generation in the final step of the homotopy.
+        inequality_constraints: A list of tuples (indices, coefficients, rhs),
+            with each tuple encoding an inequality constraint of the form
+            `\sum_i (X[indices[i]] * coefficients[i]) >= rhs`. `indices` and
+            `coefficients` should be torch tensors. See the docstring of
+            `make_scipy_linear_constraints` for an example. When q=1, or when
+            applying the same constraint to each candidate in the batch
+            (intra-point constraint), `indices` should be a 1-d tensor.
+            For inter-point constraints, in which the constraint is applied to the
+            whole batch of candidates, `indices` must be a 2-d tensor, where
+            in each row `indices[i] =(k_i, l_i)` the first index `k_i` corresponds
+            to the `k_i`-th element of the `q`-batch and the second index `l_i`
+            corresponds to the `l_i`-th feature of that element.
+        equality_constraints: A list of tuples (indices, coefficients, rhs),
+            with each tuple encoding an equality constraint of the form
+            `\sum_i (X[indices[i]] * coefficients[i]) = rhs`. See the docstring of
+            `make_scipy_linear_constraints` for an example.
+        nonlinear_inequality_constraints: A list of tuples representing the nonlinear
+            inequality constraints. The first element in the tuple is a callable
+            representing a constraint of the form `callable(x) >= 0`. In case of an
+            intra-point constraint, `callable()`takes in an one-dimensional tensor of
+            shape `d` and returns a scalar. In case of an inter-point constraint,
+            `callable()` takes a two dimensional tensor of shape `q x d` and again
+            returns a scalar. The second element is a boolean, indicating if it is an
+            intra-point or inter-point constraint (`True` for intra-point. `False` for
+            inter-point). For more information on intra-point vs inter-point
+            constraints, see the docstring of the `inequality_constraints` argument to
+            `optimize_acqf()`. The constraints will later be passed to the scipy
+            solver. You need to pass in `batch_initial_conditions` in this case.
+            Using non-linear inequality constraints also requires that `batch_limit`
+            is set to 1, which will be done automatically if not specified in
+            `options`.
         fixed_features: A map `{feature_index: value}` for features that
             should be fixed to a particular value during generation.
-        options: Options for candidate generation.
-        final_options: Options for candidate generation in the last homotopy step.
+        post_processing_func: A function that post-processes an optimization
+            result appropriately (i.e., according to `round-trip`
+            transformations).
         batch_initial_conditions: A tensor to specify the initial conditions. Set
             this if you do not want to use default initialization strategy.
-        post_processing_func: Post processing function (such as rounding or clamping)
-            that is applied before choosing the final candidate.
+        gen_candidates: A callable for generating candidates (and their associated
+            acquisition values) given a tensor of initial conditions and an
+            acquisition function. Other common inputs include lower and upper bounds
+            and a dictionary of options, but refer to the documentation of specific
+            generation functions (e.g gen_candidates_scipy and gen_candidates_torch)
+            for method-specific inputs. Default: `gen_candidates_scipy`
+        sequential: If False, uses joint optimization, otherwise uses sequential
+            optimization.
+        ic_generator: Function for generating initial conditions. Not needed when
+            `batch_initial_conditions` are provided. Defaults to
+            `gen_one_shot_kg_initial_conditions` for `qKnowledgeGradient` acquisition
+            functions and `gen_batch_initial_conditions` otherwise. Must be specified
+            for nonlinear inequality constraints.
+        timeout_sec: Max amount of time optimization can run for.
+        return_full_tree: Return the full tree of optimizers of the previous
+            iteration.
+        retry_on_optimization_warning: Whether to retry candidate generation with a new
+            set of initial conditions when it fails with an `OptimizationWarning`.
+        ic_gen_kwargs: Additional keyword arguments passed to function specified by
+            `ic_generator`
     """
+    shared_optimize_acqf_kwargs = {
+        "num_restarts": num_restarts,
+        "raw_samples": raw_samples,
+        "inequality_constraints": inequality_constraints,
+        "equality_constraints": equality_constraints,
+        "nonlinear_inequality_constraints": nonlinear_inequality_constraints,
+        "fixed_features": fixed_features,
+        "return_best_only": False,  # This is set to False so that the restarts propagate through to the final homotopy.
+        "gen_candidates": gen_candidates,
+        "sequential": sequential,
+        "ic_generator": ic_generator,
+        "timeout_sec": timeout_sec,
+        "return_full_tree": return_full_tree,
+        "retry_on_optimization_warning": retry_on_optimization_warning,
+        **ic_gen_kwargs,
+    }
+
     candidate_list, acq_value_list = [], []
     if q > 1:
         base_X_pending = acq_function.X_pending
@@ -91,15 +183,12 @@ def optimize_acqf_homotopy(
 
         while not homotopy.should_stop:
             candidates, acq_values = optimize_acqf(
-                q=1,
                 acq_function=acq_function,
                 bounds=bounds,
-                num_restarts=num_restarts,
-                batch_initial_conditions=candidates,
-                raw_samples=raw_samples,
-                fixed_features=fixed_features,
-                return_best_only=False,
+                q=1,
                 options=options,
+                batch_initial_conditions=candidates,
+                **shared_optimize_acqf_kwargs
             )
             homotopy.step()
 
@@ -112,26 +201,27 @@ def optimize_acqf_homotopy(
 
         # Optimize one more time with the final options
         candidates, acq_values = optimize_acqf(
-            q=1,
             acq_function=acq_function,
             bounds=bounds,
-            num_restarts=num_restarts,
-            batch_initial_conditions=candidates,
-            return_best_only=False,
+            q=1,
             options=final_options,
+            batch_initial_conditions=candidates,
+            **shared_optimize_acqf_kwargs,
         )
 
         # Post-process the candidates and grab the best candidate
         if post_processing_func is not None:
             candidates = post_processing_func(candidates)
             acq_values = acq_function(candidates)
+
         best = torch.argmax(acq_values.view(-1), dim=0)
         candidate, acq_value = candidates[best], acq_values[best]
 
         # Keep the new candidate and update the pending points
         candidate_list.append(candidate)
         acq_value_list.append(acq_value)
         selected_candidates = torch.cat(candidate_list, dim=-2)
+
         if q > 1:
             acq_function.set_X_pending(
                 torch.cat([base_X_pending, selected_candidates], dim=-2)
@@ -141,6 +231,7 @@ def optimize_acqf_homotopy(
 
     if q > 1:  # Reset acq_function to previous X_pending state
         acq_function.set_X_pending(base_X_pending)
+
     homotopy.reset()  # Reset the homotopy parameters
 
     return selected_candidates, torch.stack(acq_value_list)

test/optim/test_homotopy.py

@@ -157,6 +157,26 @@ def test_optimize_acqf_homotopy(self):
         self.assertEqual(candidate.shape, torch.Size([3, 2]))
         self.assertEqual(acqf_val.shape, torch.Size([3]))
 
+        # with linear constraints
+        constraints = [(  # X[..., 0] + X[..., 1] >= 2.
+            torch.tensor([0, 1], device=self.device),
+            torch.ones(2, device=self.device, dtype=torch.double),
+            2.0,
+        )]
+
+        acqf = PosteriorMean(model=model)
+        candidate, acqf_val = optimize_acqf_homotopy(
+            q=1,
+            acq_function=acqf,
+            bounds=torch.tensor([[-10, -10], [5, 5]]).to(**tkwargs),
+            homotopy=Homotopy(homotopy_parameters=[hp]),
+            num_restarts=2,
+            raw_samples=16,
+            inequality_constraints=constraints,
+        )
+        self.assertEqual(candidate.shape, torch.Size([1, 2]))
+        self.assertGreaterEqual(candidate.sum(), 2 * torch.ones(1, **tkwargs))
+
     def test_prune_candidates(self):
         tkwargs = {"device": self.device, "dtype": torch.double}
         # no pruning
@@ -210,6 +230,7 @@ def test_optimize_acqf_homotopy_pruning(self, prune_candidates_mock):
             num_restarts=4,
             raw_samples=16,
             post_processing_func=lambda x: x.round(),
+            return_full_tree=True,
         )
         # First time we expect to call `prune_candidates` with 4 candidates
         self.assertEqual(