Update versions

CHANGELOG.md

@@ -0,0 +1,23 @@
+# CHANGELOG
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](http://keepachangelog.com/)
+and this project adheres to [Semantic Versioning](http://semver.org/).
+
+## [Unreleased] - YYYY-MM-DD
+
+### Added 
+
+### Changed 
+
+### Fixed
+
+## [0.0.5] - 2025-05-27
+
+### Added 
+- This CHANGELOG, to keep track of new releases in the future. 
+
+### Changed
+- Remove unnecessary dependencies (pandas, pylgmath, gurobipy)
+- Make some dependencies optional (plotly, sparseqr)
+- More consistent use of install_requires, requirements.txt, environment.yml

_test/utils.py

@@ -1,13 +1,37 @@
 import numpy as np
 from poly_matrix import PolyMatrix
-from pylgmath import so3op
+from scipy.spatial.transform import Rotation as R
 
 from cert_tools import HomQCQP
 
 # Global Defaults
 ER_MIN = 1e6
 
 
+def vec2rot(aaxis_ba):
+    """Replacement for pylgmath.so3.operations.vec2rot"""
+    mats = []
+    if np.ndim(aaxis_ba) == 3:
+        for ai in aaxis_ba:
+            assert ai.shape[-1] == 1
+            r = R.from_rotvec(ai[:, 0])
+            mats.append(r.as_matrix())
+        mat = np.stack(mats)
+    elif np.ndim(aaxis_ba) == 2:
+        assert aaxis_ba.shape[-1] == 1
+        r = R.from_rotvec(aaxis_ba[:, 0])
+        mat = r.as_matrix()
+
+    # For performance, we can switch back to using pylgmath.
+    # For now it was removed to avoid dependeny for one function only.
+    # Below was ensured to pass before removing dependncy.
+    #
+    # from pylgmath import so3op
+    # mat_test = so3op.vec2rot(aaxis_ba)
+    # np.testing.assert_allclose(mat, mat_test)
+    return mat
+
+
 class RotSynchLoopProblem(HomQCQP):
     """Class to generate and solve a rotation synchronization problem with loop
     constraints. The problem is generated with ground truth rotations and noisy
@@ -34,7 +58,7 @@ def __init__(self, N=10, sigma=1e-3, loop_pose=3, locked_pose=0, seed=0):
         np.random.seed(seed)
         # generate ground truth poses
         aaxis_ab_rand = np.random.uniform(-np.pi / 2, np.pi / 2, size=(N, 3, 1))
-        R_gt = so3op.vec2rot(aaxis_ab_rand)
+        R_gt = vec2rot(aaxis_ab_rand)
         # Associated variable list
         self.var_sizes = {"h": 1}
         for i in range(N):
@@ -44,7 +68,7 @@ def __init__(self, N=10, sigma=1e-3, loop_pose=3, locked_pose=0, seed=0):
         self.locked_pose = str(locked_pose)  # Pose locked at this pose
         self.meas_dict = {}
         for i in range(0, N):
-            R_pert = so3op.vec2rot(sigma * np.random.randn(3, 1))
+            R_pert = vec2rot(sigma * np.random.randn(3, 1))
             if i == N - 1:
                 if loop_pose > 0:
                     j = loop_pose

cert_tools/__init__.py

@@ -4,4 +4,4 @@
 from .linalg_tools import *
 from .sdp_solvers import *
 
-__version__ = "0.0.3"
+__version__ = "0.0.5"

cert_tools/eopt_solvers.py

@@ -3,11 +3,11 @@
 import mosek
 import numpy as np
 import numpy.linalg as la
-import pandas as pd
 import scipy.sparse as sp
+from scipy.optimize import linprog
+
 from cert_tools.eig_tools import get_min_eigpairs
 from cert_tools.linalg_tools import get_nullspace
-from scipy.optimize import linprog
 
 # Number of eigenvalues to compute
 EIG_METHOD = "direct"  # "lobpcg"
@@ -474,6 +474,10 @@ def solve_eopt_cuts(
     """Solve the certificate/eigenvalue optimization problem using a cutting plane algorithm.
     Current algorithm uses the level method with the target level at a tolerance below zero
     """
+    print(
+        "Warning: when using this function, you might want to convert iter_info to a pandas Dataframe."
+        "This used to be the default behavior but was removed to avoid pandas dependency."
+    )
     # Initialize cut plane model
     m = CutPlaneModel(xinit.shape[0], A_eq=A_eq, b_eq=b_eq)
     # Intialize status vars for optimization
@@ -589,7 +593,7 @@ def solve_eopt_cuts(
         gap=gap,
         t_min=t_min,
         t_max=t_max,
-        iter_info=pd.DataFrame(iter_info),
+        iter_info=iter_info,
         model=m,
     )
 

cert_tools/hom_qcqp.py

@@ -5,8 +5,6 @@
 import igraph as ig
 import matplotlib.pyplot as plt
 import numpy as np
-import plotly.graph_objects as go
-import plotly.io as pio
 import scipy.sparse as sp
 from cvxopt import amd, spmatrix
 from igraph import Graph
@@ -735,6 +733,9 @@ def get_slices(self, mat, var_list_row, var_list_col=[]):
 
 
 def plot_graph(graph, **kwargs):
+    import plotly.graph_objects as go
+    import plotly.io as pio
+
     layout = kwargs.get("layout", graph.layout("kk"))
     vertex_label = kwargs.get(
         "vertex_label", graph.vs["name"] if "name" in graph.vs.attributes() else None

environment.yml

@@ -9,19 +9,12 @@ dependencies:
     - python=3.10
     - pip=22.3
 
-    - numpy>=1.23.5
-    - scipy==1.9.1
-    - matplotlib>=3.6.2
-    - pandas>=1.5.3
-    - cvxpy>=1.3.2
-    - pytest>=7.2.2
-    - black>=23.1.0
-    - mosek>=11
-    - casadi>=3.6.3
-    - scikit-sparse>=0.4.14
-    - plotly>=5.24.1
-    - suitesparse
+    # packages for local development that are not installed by setup.cfg
+    - black>=23.1
+    - plotly>=5.24
+    - pytest
 
+    - suitesparse # required for sparseqr
     - pip:
       - -r requirements.txt
-      - -e .  
+      - -e .

requirements.txt

@@ -1,8 +1,2 @@
-sparseqr>=1.2.1
-gurobipy==10.0.3 
-quadprog==0.1.11
-cvxopt>=1.3.2
-igraph>=0.11.8
-asrl-pylgmath>=1.0.3
-chompack>=2.3.2
-git+https://github.com/utiasASRL/poly_matrix.git@v0.2
+# packages that cannot be installed by install_requires or conda
+sparseqr

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = cert_tools
-version = 0.0.3
+version = 0.0.5
 authors = [
     {name = "Frederike Dümbgen", email = "frederike.dumbgen@utoronto.ca" },
     {name = "Connor Holmes", email = "connor.holmes@mail.utoronto.ca" }]
@@ -16,18 +16,17 @@ license = { file="LICENSE" }
 [options]
 packages = find:
 install_requires = 
-  casadi 
-  matplotlib
-  numpy
-  pandas
-  scipy
-  mosek
-  cvxpy
-  igraph
+  casadi>=3.6  # used for BM interface to IPOPT
+  cvxpy>=1.3
+  matplotlib>=3.6
+  mosek>=11
+  numpy>=1.23
+  scipy==1.9
+  igraph # for aggregate sparsity definition
   chompack
-  plotly
+  poly_matrix @ git+https://github.com/utiasASRL/poly_matrix.git@v0.3.1#egg=poly_matrix
 
-[options.packages.find] # do not mistake tests/ for a package directory
+[options.packages.find] # do not mistake _tests/ for a package directory
 exclude=_test*
 
 [flake8]