Simplify processing of multistart results.

src/optimagic/optimization/algorithm.py

@@ -305,6 +305,15 @@ def solve_internal_problem(
                 step_id, {"status": str(StepStatus.COMPLETE.value)}
             )
 
+        # make sure the start params provided in static_result_fields are the same as x0
+        extra_fields = problem.static_result_fields
+        x0_problem = problem.converter.params_to_internal(extra_fields.start_params)
+        if not np.allclose(x0_problem, x0):
+            start_params = problem.converter.params_from_internal(x0)
+            extra_fields = replace(
+                extra_fields, start_params=start_params, start_fun=None
+            )
+
         res = raw_res.create_optimize_result(
             converter=problem.converter,
             solver_type=self.algo_info.solver_type,

src/optimagic/optimization/multistart.py

@@ -170,19 +170,19 @@ def single_optimization(x0, step_id):
                     logger.step_store.update(step, {"status": new_status})
             break
 
-    multistart_info = {
-        "start_parameters": state["start_history"],
-        "local_optima": state["result_history"],
-        "exploration_sample": sorted_sample,
-        "exploration_results": exploration_res["sorted_values"],
-    }
-
     raw_res = state["best_res"]
+
+    expl_sample = [
+        internal_problem.converter.params_from_internal(s) for s in sorted_sample
+    ]
+    expl_res = list(exploration_res["sorted_values"])
+
     res = process_multistart_result(
         raw_res=raw_res,
-        converter=internal_problem.converter,
         extra_fields=internal_problem.static_result_fields,
-        multistart_info=multistart_info,
+        local_optima=state["result_history"],
+        exploration_sample=expl_sample,
+        exploration_results=expl_res,
     )
 
     return res

src/optimagic/optimization/optimize_result.py

@@ -41,7 +41,7 @@ class OptimizeResult:
 
     params: Any
     fun: float
-    start_fun: float
+    start_fun: float | None
     start_params: Any
     algorithm: str
     direction: str
@@ -78,7 +78,7 @@ def criterion(self) -> float:
         return self.fun
 
     @property
-    def start_criterion(self) -> float:
+    def start_criterion(self) -> float | None:
         msg = (
             "The start_criterion attribute is deprecated. Use the start_fun attribute "
             "instead."

src/optimagic/optimization/process_multistart_result.py

@@ -1,30 +1,32 @@
-import copy
-from typing import Any
-
 import numpy as np
+from numpy.typing import NDArray
 
 from optimagic.optimization.convergence_report import get_convergence_report
 from optimagic.optimization.optimize_result import MultistartInfo, OptimizeResult
-from optimagic.parameters.conversion import Converter
 from optimagic.typing import Direction, ExtraResultFields
 
 
 def process_multistart_result(
     raw_res: OptimizeResult,
-    converter: Converter,
     extra_fields: ExtraResultFields,
-    multistart_info: dict[str, Any],
+    local_optima: list[OptimizeResult],
+    exploration_sample: list[NDArray[np.float64]],
+    exploration_results: list[float],
 ) -> OptimizeResult:
     """Process results of internal optimizers."""
 
     if isinstance(raw_res, str):
         res = _dummy_result_from_traceback(raw_res, extra_fields)
     else:
         res = raw_res
-        info = _process_multistart_info(
-            multistart_info,
-            converter=converter,
-            extra_fields=extra_fields,
+        if extra_fields.direction == Direction.MAXIMIZE:
+            exploration_results = [-res for res in exploration_results]
+
+        info = MultistartInfo(
+            start_parameters=[opt.start_params for opt in local_optima],
+            local_optima=local_optima,
+            exploration_sample=exploration_sample,
+            exploration_results=exploration_results,
         )
 
         # ==============================================================================
@@ -53,42 +55,18 @@ def process_multistart_result(
     return res
 
 
-def _process_multistart_info(
-    info: dict[str, Any],
-    converter: Converter,
-    extra_fields: ExtraResultFields,
-) -> MultistartInfo:
-    starts = [converter.params_from_internal(x) for x in info["start_parameters"]]
-
-    optima = []
-    for res, start in zip(info["local_optima"], starts, strict=False):
-        processed = copy.copy(res)
-        processed.start_params = start
-        processed.start_fun = None
-        optima.append(processed)
-
-    sample = [converter.params_from_internal(x) for x in info["exploration_sample"]]
-
-    if extra_fields.direction == Direction.MINIMIZE:
-        exploration_res = info["exploration_results"]
-    else:
-        exploration_res = [-res for res in info["exploration_results"]]
-
-    return MultistartInfo(
-        start_parameters=starts,
-        local_optima=optima,
-        exploration_sample=sample,
-        exploration_results=exploration_res,
-    )
-
-
 def _dummy_result_from_traceback(
     candidate: str, extra_fields: ExtraResultFields
 ) -> OptimizeResult:
+    if extra_fields.start_fun is None:
+        start_fun = np.inf
+    else:
+        start_fun = extra_fields.start_fun
+
     out = OptimizeResult(
         params=extra_fields.start_params,
-        fun=extra_fields.start_fun,
-        start_fun=extra_fields.start_fun,
+        fun=start_fun,
+        start_fun=start_fun,
         start_params=extra_fields.start_params,
         algorithm=extra_fields.algorithm,
         direction=extra_fields.direction.value,

src/optimagic/typing.py

@@ -162,7 +162,7 @@ class MultiStartIterationHistory(TupleLikeAccess):
 class ExtraResultFields:
     """Fields for OptimizeResult that are not part of InternalOptimizeResult."""
 
-    start_fun: float
+    start_fun: float | None
     start_params: PyTree
     algorithm: str
     direction: Direction

src/optimagic/visualization/history_plots.py

@@ -344,7 +344,7 @@ def _extract_plotting_data_from_results_object(
                 res.multistart_info.exploration_sample[::-1] + stacked["params"]
             )
             stacked["criterion"] = (
-                res.multistart_info.exploration_results.tolist()[::-1]
+                list(res.multistart_info.exploration_results)[::-1]
                 + stacked["criterion"]
             )
     else: