diff --git a/docs/source/algorithms.md b/docs/source/algorithms.md
index c8c379b25..a09878e28 100644
--- a/docs/source/algorithms.md
+++ b/docs/source/algorithms.md
@@ -313,6 +313,35 @@ automatically installed when you install optimagic.
 
 ```
 
+```{eval-rst}
+.. dropdown::  scipy_cobyqa
+
+    **How to use this algorithm:**
+
+    .. code-block::
+
+        import optimagic as om
+        om.minimize(
+          ...,
+          algorithm=om.algos.scipy_cobyqa(stopping_maxfun=1_000, ...)
+        )
+
+    or
+
+    .. code-block::
+
+        om.minimize(
+          ...,
+          algorithm="scipy_cobyqa",
+          algo_options={"stopping_maxfun": 1_000, ...}
+        )
+
+    **Description and available options:**
+
+    .. autoclass:: optimagic.optimizers.scipy_optimizers.ScipyCOBYQA
+
+```
+
 ```{eval-rst}
 .. dropdown::  scipy_truncated_newton
 
diff --git a/src/optimagic/algorithms.py b/src/optimagic/algorithms.py
index 795046950..33be900d9 100644
--- a/src/optimagic/algorithms.py
+++ b/src/optimagic/algorithms.py
@@ -96,6 +96,7 @@
     ScipyBFGS,
     ScipyBrute,
     ScipyCOBYLA,
+    ScipyCOBYQA,
     ScipyConjugateGradient,
     ScipyDifferentialEvolution,
     ScipyDirect,
@@ -214,7 +215,7 @@ class BoundedGlobalGradientFreeParallelScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -254,6 +255,7 @@ def Bounded(
 @dataclass(frozen=True)
 class BoundedGradientFreeLocalNonlinearConstrainedScalarAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
 
 
 @dataclass(frozen=True)
@@ -441,7 +443,7 @@ class BoundedGlobalGradientFreeScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -497,7 +499,7 @@ class BoundedGlobalGradientFreeParallelAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -569,7 +571,7 @@ class GlobalGradientFreeParallelScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -600,6 +602,7 @@ def NonlinearConstrained(
 @dataclass(frozen=True)
 class BoundedGradientFreeLocalNonlinearConstrainedAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
 
     @property
     def Scalar(self) -> BoundedGradientFreeLocalNonlinearConstrainedScalarAlgorithms:
@@ -614,6 +617,7 @@ class BoundedGradientFreeLocalScalarAlgorithms(AlgoSelection):
     nlopt_newuoa: Type[NloptNEWUOA] = NloptNEWUOA
     nlopt_neldermead: Type[NloptNelderMead] = NloptNelderMead
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_neldermead: Type[ScipyNelderMead] = ScipyNelderMead
     scipy_powell: Type[ScipyPowell] = ScipyPowell
     tranquilo: Type[Tranquilo] = Tranquilo
@@ -660,6 +664,7 @@ def Scalar(self) -> BoundedGradientFreeLocalParallelScalarAlgorithms:
 class GradientFreeLocalNonlinearConstrainedScalarAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
 
     @property
     def Bounded(self) -> BoundedGradientFreeLocalNonlinearConstrainedScalarAlgorithms:
@@ -690,6 +695,7 @@ def Bounded(self) -> BoundedGradientFreeLeastSquaresLocalParallelAlgorithms:
 class BoundedGradientFreeNonlinearConstrainedScalarAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -733,7 +739,7 @@ class BoundedGradientFreeParallelScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -844,7 +850,7 @@ class BoundedGlobalParallelScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -895,6 +901,7 @@ class BoundedLocalNonlinearConstrainedScalarAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
     nlopt_mma: Type[NloptMMA] = NloptMMA
     nlopt_slsqp: Type[NloptSLSQP] = NloptSLSQP
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_slsqp: Type[ScipySLSQP] = ScipySLSQP
     scipy_trust_constr: Type[ScipyTrustConstr] = ScipyTrustConstr
 
@@ -1204,7 +1211,7 @@ class BoundedGlobalGradientFreeAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -1301,7 +1308,7 @@ class GlobalGradientFreeScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -1361,7 +1368,7 @@ class GlobalGradientFreeParallelAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -1403,6 +1410,7 @@ class BoundedGradientFreeLocalAlgorithms(AlgoSelection):
     nlopt_neldermead: Type[NloptNelderMead] = NloptNelderMead
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
     pounders: Type[Pounders] = Pounders
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_neldermead: Type[ScipyNelderMead] = ScipyNelderMead
     scipy_powell: Type[ScipyPowell] = ScipyPowell
     tao_pounders: Type[TAOPounders] = TAOPounders
@@ -1432,6 +1440,7 @@ def Scalar(self) -> BoundedGradientFreeLocalScalarAlgorithms:
 class GradientFreeLocalNonlinearConstrainedAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
 
     @property
     def Bounded(self) -> BoundedGradientFreeLocalNonlinearConstrainedAlgorithms:
@@ -1453,6 +1462,7 @@ class GradientFreeLocalScalarAlgorithms(AlgoSelection):
     nlopt_praxis: Type[NloptPRAXIS] = NloptPRAXIS
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_neldermead: Type[ScipyNelderMead] = ScipyNelderMead
     scipy_powell: Type[ScipyPowell] = ScipyPowell
     tranquilo: Type[Tranquilo] = Tranquilo
@@ -1512,6 +1522,7 @@ def Scalar(self) -> GradientFreeLocalParallelScalarAlgorithms:
 class BoundedGradientFreeNonlinearConstrainedAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
     nlopt_isres: Type[NloptISRES] = NloptISRES
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -1560,7 +1571,7 @@ class BoundedGradientFreeScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -1596,6 +1607,7 @@ class BoundedGradientFreeScalarAlgorithms(AlgoSelection):
     pyswarms_global_best: Type[PySwarmsGlobalBestPSO] = PySwarmsGlobalBestPSO
     pyswarms_local_best: Type[PySwarmsLocalBestPSO] = PySwarmsLocalBestPSO
     scipy_brute: Type[ScipyBrute] = ScipyBrute
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -1648,7 +1660,7 @@ class BoundedGradientFreeParallelAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -1696,6 +1708,7 @@ class GradientFreeNonlinearConstrainedScalarAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
     nlopt_isres: Type[NloptISRES] = NloptISRES
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -1746,7 +1759,7 @@ class GradientFreeParallelScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -1848,7 +1861,7 @@ class BoundedGlobalScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -1913,7 +1926,7 @@ class BoundedGlobalParallelAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -1998,7 +2011,7 @@ class GlobalParallelScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -2036,6 +2049,7 @@ class BoundedLocalNonlinearConstrainedAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
     nlopt_mma: Type[NloptMMA] = NloptMMA
     nlopt_slsqp: Type[NloptSLSQP] = NloptSLSQP
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_slsqp: Type[ScipySLSQP] = ScipySLSQP
     scipy_trust_constr: Type[ScipyTrustConstr] = ScipyTrustConstr
 
@@ -2069,6 +2083,7 @@ class BoundedLocalScalarAlgorithms(AlgoSelection):
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
     nlopt_tnewton: Type[NloptTNewton] = NloptTNewton
     nlopt_var: Type[NloptVAR] = NloptVAR
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_lbfgsb: Type[ScipyLBFGSB] = ScipyLBFGSB
     scipy_neldermead: Type[ScipyNelderMead] = ScipyNelderMead
     scipy_powell: Type[ScipyPowell] = ScipyPowell
@@ -2142,6 +2157,7 @@ class LocalNonlinearConstrainedScalarAlgorithms(AlgoSelection):
     nlopt_mma: Type[NloptMMA] = NloptMMA
     nlopt_slsqp: Type[NloptSLSQP] = NloptSLSQP
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_slsqp: Type[ScipySLSQP] = ScipySLSQP
     scipy_trust_constr: Type[ScipyTrustConstr] = ScipyTrustConstr
 
@@ -2193,6 +2209,7 @@ class BoundedNonlinearConstrainedScalarAlgorithms(AlgoSelection):
     nlopt_isres: Type[NloptISRES] = NloptISRES
     nlopt_mma: Type[NloptMMA] = NloptMMA
     nlopt_slsqp: Type[NloptSLSQP] = NloptSLSQP
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -2249,7 +2266,7 @@ class BoundedParallelScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -2541,7 +2558,7 @@ class GlobalGradientFreeAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -2607,6 +2624,7 @@ class GradientFreeLocalAlgorithms(AlgoSelection):
     nlopt_sbplx: Type[NloptSbplx] = NloptSbplx
     pounders: Type[Pounders] = Pounders
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_neldermead: Type[ScipyNelderMead] = ScipyNelderMead
     scipy_powell: Type[ScipyPowell] = ScipyPowell
     tao_pounders: Type[TAOPounders] = TAOPounders
@@ -2662,7 +2680,7 @@ class BoundedGradientFreeAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -2699,6 +2717,7 @@ class BoundedGradientFreeAlgorithms(AlgoSelection):
     pyswarms_global_best: Type[PySwarmsGlobalBestPSO] = PySwarmsGlobalBestPSO
     pyswarms_local_best: Type[PySwarmsLocalBestPSO] = PySwarmsLocalBestPSO
     scipy_brute: Type[ScipyBrute] = ScipyBrute
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -2739,6 +2758,7 @@ class GradientFreeNonlinearConstrainedAlgorithms(AlgoSelection):
     nlopt_cobyla: Type[NloptCOBYLA] = NloptCOBYLA
     nlopt_isres: Type[NloptISRES] = NloptISRES
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -2792,7 +2812,7 @@ class GradientFreeScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -2830,6 +2850,7 @@ class GradientFreeScalarAlgorithms(AlgoSelection):
     pyswarms_local_best: Type[PySwarmsLocalBestPSO] = PySwarmsLocalBestPSO
     scipy_brute: Type[ScipyBrute] = ScipyBrute
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -2889,7 +2910,7 @@ class GradientFreeParallelAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -2962,7 +2983,7 @@ class BoundedGlobalAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -3077,7 +3098,7 @@ class GlobalScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -3146,7 +3167,7 @@ class GlobalParallelAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -3199,6 +3220,7 @@ class BoundedLocalAlgorithms(AlgoSelection):
     nlopt_tnewton: Type[NloptTNewton] = NloptTNewton
     nlopt_var: Type[NloptVAR] = NloptVAR
     pounders: Type[Pounders] = Pounders
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_lbfgsb: Type[ScipyLBFGSB] = ScipyLBFGSB
     scipy_ls_dogbox: Type[ScipyLSDogbox] = ScipyLSDogbox
     scipy_ls_trf: Type[ScipyLSTRF] = ScipyLSTRF
@@ -3243,6 +3265,7 @@ class LocalNonlinearConstrainedAlgorithms(AlgoSelection):
     nlopt_mma: Type[NloptMMA] = NloptMMA
     nlopt_slsqp: Type[NloptSLSQP] = NloptSLSQP
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_slsqp: Type[ScipySLSQP] = ScipySLSQP
     scipy_trust_constr: Type[ScipyTrustConstr] = ScipyTrustConstr
 
@@ -3284,6 +3307,7 @@ class LocalScalarAlgorithms(AlgoSelection):
     nlopt_var: Type[NloptVAR] = NloptVAR
     scipy_bfgs: Type[ScipyBFGS] = ScipyBFGS
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_conjugate_gradient: Type[ScipyConjugateGradient] = ScipyConjugateGradient
     scipy_lbfgsb: Type[ScipyLBFGSB] = ScipyLBFGSB
     scipy_neldermead: Type[ScipyNelderMead] = ScipyNelderMead
@@ -3382,6 +3406,7 @@ class BoundedNonlinearConstrainedAlgorithms(AlgoSelection):
     nlopt_isres: Type[NloptISRES] = NloptISRES
     nlopt_mma: Type[NloptMMA] = NloptMMA
     nlopt_slsqp: Type[NloptSLSQP] = NloptSLSQP
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -3444,7 +3469,7 @@ class BoundedScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -3487,6 +3512,7 @@ class BoundedScalarAlgorithms(AlgoSelection):
     pyswarms_local_best: Type[PySwarmsLocalBestPSO] = PySwarmsLocalBestPSO
     scipy_basinhopping: Type[ScipyBasinhopping] = ScipyBasinhopping
     scipy_brute: Type[ScipyBrute] = ScipyBrute
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -3561,7 +3587,7 @@ class BoundedParallelAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -3614,6 +3640,7 @@ class NonlinearConstrainedScalarAlgorithms(AlgoSelection):
     nlopt_mma: Type[NloptMMA] = NloptMMA
     nlopt_slsqp: Type[NloptSLSQP] = NloptSLSQP
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -3679,7 +3706,7 @@ class ParallelScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -3820,7 +3847,7 @@ class GradientFreeAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -3859,6 +3886,7 @@ class GradientFreeAlgorithms(AlgoSelection):
     pyswarms_local_best: Type[PySwarmsLocalBestPSO] = PySwarmsLocalBestPSO
     scipy_brute: Type[ScipyBrute] = ScipyBrute
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -3924,7 +3952,7 @@ class GlobalAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -4012,6 +4040,7 @@ class LocalAlgorithms(AlgoSelection):
     pounders: Type[Pounders] = Pounders
     scipy_bfgs: Type[ScipyBFGS] = ScipyBFGS
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_conjugate_gradient: Type[ScipyConjugateGradient] = ScipyConjugateGradient
     scipy_lbfgsb: Type[ScipyLBFGSB] = ScipyLBFGSB
     scipy_ls_dogbox: Type[ScipyLSDogbox] = ScipyLSDogbox
@@ -4091,7 +4120,7 @@ class BoundedAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -4135,6 +4164,7 @@ class BoundedAlgorithms(AlgoSelection):
     pyswarms_local_best: Type[PySwarmsLocalBestPSO] = PySwarmsLocalBestPSO
     scipy_basinhopping: Type[ScipyBasinhopping] = ScipyBasinhopping
     scipy_brute: Type[ScipyBrute] = ScipyBrute
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -4194,6 +4224,7 @@ class NonlinearConstrainedAlgorithms(AlgoSelection):
     nlopt_mma: Type[NloptMMA] = NloptMMA
     nlopt_slsqp: Type[NloptSLSQP] = NloptSLSQP
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
     )
@@ -4261,7 +4292,7 @@ class ScalarAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -4307,6 +4338,7 @@ class ScalarAlgorithms(AlgoSelection):
     scipy_basinhopping: Type[ScipyBasinhopping] = ScipyBasinhopping
     scipy_brute: Type[ScipyBrute] = ScipyBrute
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_conjugate_gradient: Type[ScipyConjugateGradient] = ScipyConjugateGradient
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
@@ -4406,7 +4438,7 @@ class ParallelAlgorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -4488,7 +4520,7 @@ class Algorithms(AlgoSelection):
     nevergrad_eda: Type[NevergradEDA] = NevergradEDA
     nevergrad_emna: Type[NevergradEMNA] = NevergradEMNA
     nevergrad_meta: Type[NevergradMeta] = NevergradMeta
-    nevergrad_NGOpt: Type[NevergradNGOpt] = NevergradNGOpt
+    nevergrad_ngopt: Type[NevergradNGOpt] = NevergradNGOpt
     nevergrad_oneplusone: Type[NevergradOnePlusOne] = NevergradOnePlusOne
     nevergrad_pso: Type[NevergradPSO] = NevergradPSO
     nevergrad_randomsearch: Type[NevergradRandomSearch] = NevergradRandomSearch
@@ -4535,6 +4567,7 @@ class Algorithms(AlgoSelection):
     scipy_basinhopping: Type[ScipyBasinhopping] = ScipyBasinhopping
     scipy_brute: Type[ScipyBrute] = ScipyBrute
     scipy_cobyla: Type[ScipyCOBYLA] = ScipyCOBYLA
+    scipy_cobyqa: Type[ScipyCOBYQA] = ScipyCOBYQA
     scipy_conjugate_gradient: Type[ScipyConjugateGradient] = ScipyConjugateGradient
     scipy_differential_evolution: Type[ScipyDifferentialEvolution] = (
         ScipyDifferentialEvolution
diff --git a/src/optimagic/config.py b/src/optimagic/config.py
index e75f11f62..4dfcbab9c 100644
--- a/src/optimagic/config.py
+++ b/src/optimagic/config.py
@@ -3,6 +3,7 @@
 
 import pandas as pd
 import plotly.express as px
+import scipy
 from packaging import version
 
 DOCS_DIR = Path(__file__).parent.parent / "docs"
@@ -79,6 +80,10 @@ def _is_installed(module_name: str) -> bool:
 # Check if pandas version is newer or equal to version 2.1.0
 # ======================================================================================
 
-IS_PANDAS_VERSION_NEWER_OR_EQUAL_TO_2_1_0 = version.parse(
-    pd.__version__
-) >= version.parse("2.1.0")
+IS_PANDAS_VERSION_GE_2_1 = version.parse(pd.__version__) >= version.parse("2.1.0")
+
+# ======================================================================================
+# Check SciPy Version for COBYQA support (added in scipy 1.14.0)
+# ======================================================================================
+
+IS_SCIPY_GE_1_14 = version.parse(scipy.__version__) >= version.parse("1.14.0")
diff --git a/src/optimagic/optimization/scipy_aliases.py b/src/optimagic/optimization/scipy_aliases.py
index fd9d1d107..25e54e0b4 100644
--- a/src/optimagic/optimization/scipy_aliases.py
+++ b/src/optimagic/optimization/scipy_aliases.py
@@ -14,6 +14,7 @@ def map_method_to_algorithm(method):
         "L-BFGS-B": "scipy_lbfgsb",
         "TNC": "scipy_truncated_newton",
         "COBYLA": "scipy_cobyla",
+        "COBYQA": "scipy_cobyqa",
         "SLSQP": "scipy_slsqp",
         "trust-constr": "scipy_trust_constr",
     }
@@ -23,7 +24,6 @@ def map_method_to_algorithm(method):
         "trust-ncg": "scipy_trust_ncg",
         "trust-exact": "scipy_trust_exact",
         "trust-krylov": "scipy_trust_krylov",
-        "COBYQA": "scipy_cobyqa",
     }
 
     if method in implemented:
diff --git a/src/optimagic/optimizers/scipy_optimizers.py b/src/optimagic/optimizers/scipy_optimizers.py
index b35aef29d..42f7d715d 100644
--- a/src/optimagic/optimizers/scipy_optimizers.py
+++ b/src/optimagic/optimizers/scipy_optimizers.py
@@ -49,6 +49,7 @@
 
 from optimagic import mark
 from optimagic.batch_evaluators import process_batch_evaluator
+from optimagic.config import IS_SCIPY_GE_1_14
 from optimagic.optimization.algo_options import (
     CONVERGENCE_FTOL_ABS,
     CONVERGENCE_FTOL_REL,
@@ -498,6 +499,126 @@ def _solve_internal_problem(
         return res
 
 
+@mark.minimizer(
+    name="scipy_cobyqa",
+    solver_type=AggregationLevel.SCALAR,
+    is_available=IS_SCIPY_GE_1_14,
+    is_global=False,
+    needs_jac=False,
+    needs_hess=False,
+    needs_bounds=False,
+    supports_parallelism=False,
+    supports_bounds=True,
+    supports_infinite_bounds=True,
+    supports_linear_constraints=False,
+    supports_nonlinear_constraints=True,
+    disable_history=False,
+)
+@dataclass(frozen=True)
+class ScipyCOBYQA(Algorithm):
+    """Minimize a scalar function using the COBYQA algorithm.
+
+    COBYQA (Constrained Optimization BY Quadratic Approximations) is a
+    derivative-free optimization algorithm for constrained problems. It is the
+    successor to COBYLA and was developed by the same research group.
+
+    The algorithm builds quadratic models of the objective function and constraints
+    using interpolation. It then solves a trust-region subproblem to find the next
+    iterate. Unlike COBYLA, which uses linear approximations, COBYQA's quadratic
+    models generally provide better convergence properties.
+
+    COBYQA is well suited for optimization problems where derivatives are unavailable
+    or expensive to compute, and where the number of variables is moderate (up to a
+    few hundred). It can handle bound constraints and general nonlinear constraints.
+
+    Key advantages over COBYLA:
+
+    - Supports bound constraints directly (COBYLA does not support bounds)
+    - Uses quadratic models instead of linear, improving convergence
+    - Generally requires fewer function evaluations to reach the same accuracy
+
+    The algorithm treats bounds as unrelaxable constraints, meaning it will never
+    evaluate the objective function outside the specified bounds.
+
+    This algorithm requires scipy >= 1.14.0.
+
+    """
+
+    stopping_maxfun: PositiveInt = STOPPING_MAXFUN
+    """Maximum number of function evaluations."""
+
+    stopping_maxiter: PositiveInt = STOPPING_MAXITER
+    """Maximum number of iterations."""
+
+    convergence_ftol_abs: NonNegativeFloat | None = None
+    """Target objective function value.
+
+    The algorithm stops when the objective function value is below this threshold.
+    Disabled by default.
+
+    """
+
+    feasibility_tol: NonNegativeFloat = 1e-8
+    """Tolerance for the constraint violation."""
+
+    trustregion_initial_radius: PositiveFloat | None = None
+    """Initial trust-region radius.
+
+    If not specified, it is calculated based on the starting point.
+
+    """
+
+    trustregion_final_radius: PositiveFloat = 1e-6
+    """Final trust-region radius, indicating the required accuracy in the variables."""
+
+    scale: bool = False
+    """Whether to scale the variables to the bounds.
+
+    If True and all bounds are finite, the variables are scaled to the range [-1, 1].
+
+    """
+
+    display: bool = False
+    """Whether to print information about the optimization progress."""
+
+    def _solve_internal_problem(
+        self, problem: InternalOptimizationProblem, x0: NDArray[np.float64]
+    ) -> InternalOptimizeResult:
+        if self.trustregion_initial_radius is None:
+            radius = calculate_trustregion_initial_radius(x0)
+        else:
+            radius = self.trustregion_initial_radius
+
+        options: dict[str, Any] = {
+            "maxfev": self.stopping_maxfun,
+            "maxiter": self.stopping_maxiter,
+            "initial_tr_radius": radius,
+            "final_tr_radius": self.trustregion_final_radius,
+            "feasibility_tol": self.feasibility_tol,
+            "scale": self.scale,
+            "disp": self.display,
+        }
+
+        if self.convergence_ftol_abs is not None:
+            options["f_target"] = self.convergence_ftol_abs
+
+        # COBYQA uses NonlinearConstraint format like trust-constr
+        nonlinear_constraints = _get_scipy_constraints(
+            equality_as_inequality_constraints(problem.nonlinear_constraints)
+        )
+
+        raw_res = scipy.optimize.minimize(
+            fun=problem.fun,
+            x0=x0,
+            method="COBYQA",
+            bounds=_get_scipy_bounds(problem.bounds),
+            constraints=nonlinear_constraints,
+            options=options,
+        )
+        res = process_scipy_result(raw_res)
+        return res
+
+
 @mark.minimizer(
     name="scipy_ls_trf",
     solver_type=AggregationLevel.LEAST_SQUARES,
diff --git a/src/optimagic/shared/compat.py b/src/optimagic/shared/compat.py
index 324d2e511..9a5148649 100644
--- a/src/optimagic/shared/compat.py
+++ b/src/optimagic/shared/compat.py
@@ -5,7 +5,7 @@
 
 """
 
-from optimagic.config import IS_PANDAS_VERSION_NEWER_OR_EQUAL_TO_2_1_0
+from optimagic.config import IS_PANDAS_VERSION_GE_2_1
 
 
 def pd_df_map(df, func, na_action=None, **kwargs):
@@ -26,7 +26,7 @@ def pd_df_map(df, func, na_action=None, **kwargs):
         pd.DataFrame: Transformed DataFrame.
 
     """
-    if IS_PANDAS_VERSION_NEWER_OR_EQUAL_TO_2_1_0:
+    if IS_PANDAS_VERSION_GE_2_1:
         out = df.map(func, na_action=na_action, **kwargs)
     else:
         out = df.applymap(func, na_action=na_action, **kwargs)