Remove the everest restart functionality

The restart functionality was added to support a research project.
However it turned out to be unnecessary for that project. Since
functionality should not be added for such a purpose only and the
general usefulness of the feature is unclear it should be removed.

docs/everest/config_generated.rst

@@ -577,47 +577,6 @@ Optimizer options
     The default is to use parallel evaluation if supported.
 
 
-**restart (optional)**
-    Type: *Optional[RestartConfig]*
-
-    Optional restarting configuration.
-
-    Restarting the optimization from scratch from a new initial point can be
-    beneficial to the optimization process for some optimization algorithms. This
-    option can be used to direct Everest to restart the optimization once or
-    multiple times.
-
-    **max_restarts (optional)**
-        Type: *int*
-
-        The maximum number of restarts.
-
-        Sets the maximum number of times that the optimization process will be
-        restarted.
-
-        The default is equal to a single restart.
-
-
-    **restart_from (required)**
-        Type: *Literal['initial', 'last', 'optimal', 'last_optimal']*
-
-        Restart from the initial, optimal or the last controls.
-
-        When restarting, the initial values for the new run are set according to this field:
-        - initial: Use the initial controls from the configuration
-        - last: Use the last controls used by the previous run
-        - optimal: Use the controls from the optimal solution found so far
-        - last_optimal: Use the controls from the optimal solution found in previous run
-
-        When restarting from optimal values, the best result obtained so far (either
-        overall, or in the last restart run) is used, which is defined as the result
-        with the maximal weighted total objective value. If the `constraint_tolerance`
-        option is set in the `optimization` section, this tolerance will be used to
-        exclude results that violate a constraint.
-
-
-
-
 
 objective_functions (required)
 ------------------------------

src/everest/config/__init__.py

@@ -15,7 +15,6 @@
 from .objective_function_config import ObjectiveFunctionConfig
 from .optimization_config import OptimizationConfig
 from .output_constraint_config import OutputConstraintConfig
-from .restart_config import RestartConfig
 from .sampler_config import SamplerConfig
 from .server_config import ServerConfig
 from .simulator_config import SimulatorConfig
@@ -38,7 +37,6 @@
     "ObjectiveFunctionConfig",
     "OptimizationConfig",
     "OutputConstraintConfig",
-    "RestartConfig",
     "SamplerConfig",
     "ServerConfig",
     "SimulatorConfig",

src/everest/config/optimization_config.py

@@ -3,7 +3,6 @@
 from pydantic import BaseModel, Field, model_validator
 
 from everest.config.cvar_config import CVaRConfig
-from everest.config.restart_config import RestartConfig
 from everest.optimizer.utils import get_ropt_plugin_manager
 
 
@@ -194,16 +193,6 @@ class OptimizationConfig(BaseModel, extra="forbid"):  # type: ignore
 parallel, if supported by the optimization algorithm.
 
 The default is to use parallel evaluation if supported.
-""",
-    )
-    restart: Optional[RestartConfig] = Field(
-        default=None,
-        description="""Optional restarting configuration.
-
-Restarting the optimization from scratch from a new initial point can be
-beneficial to the optimization process for some optimization algorithms. This
-option can be used to direct Everest to restart the optimization once or
-multiple times.
 """,
     )
 

src/everest/suite.py

@@ -443,14 +443,6 @@ def _configure_optimizer(self, simulator: Simulator) -> OptimizationPlanRunner:
             seed=self._config.environment.random_seed,
         )
 
-        # Configure restarting:
-        if self.config.optimization.restart is not None:
-            optimizer.repeat(
-                iterations=self.config.optimization.restart.max_restarts + 1,
-                restart_from=self.config.optimization.restart.restart_from,
-                metadata_var="restart",
-            )
-
         # Initialize output tables. `min_header_len` is set to ensure that all
         # tables have the same number of header lines, simplifying code that
         # reads them as fixed width tables. `maximize` is set because ropt

tests/everest/test_simulator_cache.py

@@ -0,0 +1,60 @@
+import numpy as np
+from ropt.plan import OptimizationPlanRunner
+
+from everest.config import EverestConfig, SimulatorConfig
+from everest.optimizer.everest2ropt import everest2ropt
+from everest.simulator import Simulator
+from tests.everest.utils import relpath, tmp
+
+CONFIG_PATH = relpath("..", "..", "test-data", "everest", "math_func")
+CONFIG_FILE = "config_advanced_scipy.yml"
+
+
+def test_simulator_cache(monkeypatch):
+    n_evals = 0
+    original_call = Simulator.__call__
+
+    def new_call(*args):
+        nonlocal n_evals
+        result = original_call(*args)
+        n_evals += (result.evaluation_ids >= 0).sum()
+        return result
+
+    monkeypatch.setattr(Simulator, "__call__", new_call)
+
+    with tmp(CONFIG_PATH):
+        config = EverestConfig.load_file(CONFIG_FILE)
+        config.simulator = SimulatorConfig(enable_cache=True)
+
+        ropt_config = everest2ropt(config)
+        simulator = Simulator(config)
+
+        # Run once, populating the cache of the simulator:
+        variables1 = (
+            OptimizationPlanRunner(
+                enopt_config=ropt_config,
+                evaluator=simulator,
+                seed=config.environment.random_seed,
+            )
+            .run()
+            .variables
+        )
+        assert variables1 is not None
+        assert np.allclose(variables1, [0.1, 0, 0.4], atol=0.02)
+        assert n_evals > 0
+
+        # Run again with the same simulator:
+        n_evals = 0
+        variables2 = (
+            OptimizationPlanRunner(
+                enopt_config=ropt_config,
+                evaluator=simulator,
+                seed=config.environment.random_seed,
+            )
+            .run()
+            .variables
+        )
+        assert variables2 is not None
+        assert n_evals == 0
+
+        assert np.array_equal(variables1, variables2)