python/ Implement generate_sequence()

bibkeys.txt

@@ -18,6 +18,7 @@ EmmFon2011emo
 FanWan1994numtheory
 FonGueLopPaq2011emo
 FonPaqLop06:hypervolume
+Gla2017fast
 GruFon2009:emaa
 Grunert01
 GueFon2017hv4d

python/doc/source/REFERENCES.bib

@@ -653,6 +653,34 @@ @inproceedings{FonPaqLop06:hypervolume
                   exponent even further.}
 }
 
+@incollection{Gla2017fast,
+  address = { Cham, Switzerland},
+  series = {Lecture Notes in Computer Science},
+  volume = 10173,
+  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2017},
+  publisher = {Springer International Publishing},
+  year = 2017,
+  editor = { Heike Trautmann  and  G{\"u}nther Rudolph  and  Kathrin Klamroth  and  Oliver Sch{\"u}tze  and  Margaret M. Wiecek  and  Yaochu Jin  and Christian Grimme},
+  author = { T. Glasmachers },
+  title = {A Fast Incremental {BSP} Tree Archive for Non-dominated
+                  Points},
+  pages = {252--266},
+  doi = {10.1007/978-3-319-54157-0_18},
+  abstract = {Maintaining an archive of all non-dominated points is a
+                  standard task in multi-objective optimization. Sometimes it
+                  is sufficient to store all evaluated points and to obtain the
+                  non-dominated subset in a post-processing step. Alternatively
+                  the non-dominated set can be updated on the fly. While
+                  keeping track of many non-dominated points efficiently is
+                  easy for two objectives, we propose an efficient algorithm
+                  based on a binary space partitioning BSP tree for the general
+                  case of three or more objectives. Our analysis and our
+                  empirical results demonstrate the superiority of the method
+                  over the brute-force baseline method, as well as graceful
+                  scaling to large numbers of objectives.},
+  keywords = {archiving}
+}
+
 @incollection{GruFon2009:emaa,
   editor = { Thomas Bartz-Beielstein  and  Marco Chiarandini  and  Lu{\'i}s Paquete  and  Mike Preuss },
   year = 2010,

python/doc/source/reference/functions.io.rst

@@ -30,3 +30,4 @@ Generate data
    :toctree: generated/
 
    generate_ndset
+   generate_sequence

python/doc/source/whatsnew/index.rst

@@ -11,6 +11,7 @@ Version 0.4.0
 - :func:`~moocore.vorob_t` returns a :class:`~typing.NamedTuple` instead of a dictionary.
 - :func:`~moocore.is_nondominated` is up to 10x faster in some inputs thanks to a customized radixsort implementation.
 - New shapes ``"cliff-concave"`` and ``"cliff-convex"`` added to :func:`~moocore.generate_ndset`.
+- New function :func:`~moocore.generate_sequence` to generate sequences of dominated and nondominated points.
 
 
 Version 0.3.2 (11/07/2026)