feat(aggregation): Add PCD

CHANGELOG.md

@@ -8,6 +8,15 @@ changelog does not include internal changes that do not affect the user.
 
 ## [Unreleased]
 
+### Added
+
+- Added `PCD` and `PCDWeighting` from [Not All Objectives Are Born Equal: Priority-Constrained
+  Descent for Hierarchical Multi-Objective Optimization](https://openreview.net/forum?id=HT01yGHLEt)
+  (TMLR 2026). The first row of the Jacobian is treated as the primary objective: `PCD` follows its
+  gradient as closely as possible, subject to each other objective receiving at least a fraction
+  `tau` of normalized first-order progress. `PCD` and `PCDWeighting` are stateful: they normalize
+  the gradients by a bias-corrected moving average of their squared norms.
+
 ## [0.17.1] - 2026-09-23
 
 ### Fixed

docs/source/docs/aggregation/index.rst

@@ -40,6 +40,7 @@ Abstract base classes
     mgda.rst
     modo.rst
     nash_mtl.rst
+    pcd.rst
     pcgrad.rst
     random.rst
     sdmgrad.rst

docs/source/docs/aggregation/pcd.rst

@@ -0,0 +1,10 @@
+:hide-toc:
+
+PCD
+===
+
+.. autoclass:: torchjd.aggregation.PCD
+    :members: __call__, reset
+
+.. autoclass:: torchjd.aggregation.PCDWeighting
+    :members: __call__, reset

src/torchjd/aggregation/__init__.py

@@ -55,6 +55,7 @@
 from ._mgda import MGDA, MGDAWeighting
 from ._modo import MoDoWeighting
 from ._nash_mtl import NashMTL
+from ._pcd import PCD, PCDWeighting
 from ._pcgrad import PCGrad, PCGradWeighting
 from ._random import Random, RandomWeighting
 from ._sdmgrad import SDMGradWeighting
@@ -93,6 +94,8 @@
     "MGDAWeighting",
     "MoDoWeighting",
     "NashMTL",
+    "PCD",
+    "PCDWeighting",
     "PCGrad",
     "PCGradWeighting",
     "Random",