v0.9 updates

Author: goulart-paul

Project.toml

@@ -1,7 +1,7 @@
 name = "ClarabelDocs"
 uuid = "5bffead5-e986-4d40-a894-38d9eb53c10f"
 authors = ["Paul Goulart <paul.goulart@eng.ox.ac.uk>"]
-version = "0.8.1"
+version = "0.9.0"
 
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"

docs/src/api_jl.md

@@ -6,6 +6,8 @@
 Clarabel.Solver
 Clarabel.setup!
 Clarabel.solve!
+Clarabel.save_to_file
+Clarabel.load_from_file
 ```
 
 ## [SupportedCone](@id api-cones)

docs/src/literate/convex_jl.jl

@@ -30,4 +30,4 @@ objective = 3square(x[1]) + 2square(x[2]) - x[1] - 4x[2]
 problem = minimize(objective; numeric_type = BigFloat)
 problem.constraints = [x[1] == 2x[2]]
 problem.constraints += [x >= -1; x <= 1]
-solve!(problem, Clarabel.Optimizer{BigFloat}; silent_solver = false)
+solve!(problem, Clarabel.Optimizer{BigFloat}; silent = false)

examples/jl/example_qp.jl

@@ -83,7 +83,7 @@ x = Variable(2)
 problem = minimize(3square(x[1]) + 2square(x[2]) - x[1] - 4x[2])
 problem.constraints = [x[1] == 2x[2]]
 problem.constraints += [x >= -1; x <= 1]
-solve!(problem, Clarabel.Optimizer; silent_solver = false)
+solve!(problem, Clarabel.Optimizer; silent = false)
 
 # Here is our solution
 

examples/jl/example_socp.jl

@@ -74,7 +74,7 @@ using Clarabel, Convex
 x = Variable(2)
 problem = minimize(square(x[2]))
 problem.constraints = [norm([2x[1];x[2]] - [2;2], 2) <= 1]
-solve!(problem, Clarabel.Optimizer; silent_solver = false)
+solve!(problem, Clarabel.Optimizer; silent = false)
 
 # Here is our solution
 

examples/py/example_qp.py

@@ -3,21 +3,24 @@
 from scipy import sparse
 
 # Define problem data
-P = sparse.csc_matrix([[0., 0.], [0, 0]])
+P = sparse.csc_matrix([[6., 0.], [0., 4.]])
 P = sparse.triu(P).tocsc()
 
 q = np.array([-1., -4.])
 
 A = sparse.csc_matrix(
-    [[1., -2.],        # <-- LHS of equality constraint (lower bound)
-     [1.,  0.],        # <-- LHS of inequality constraint (upper bound)
-     [0.,  1.],        # <-- LHS of inequality constraint (upper bound)
+    [[ 1., -2.],        # <-- LHS of equality constraint (lower bound)
+     [ 1.,  0.],        # <-- LHS of inequality constraint (upper bound)
+     [ 0.,  1.],        # <-- LHS of inequality constraint (upper bound)
      [-1.,  0.],       # <-- LHS of inequality constraint (lower bound)
-     [0., -1.]])       # <-- LHS of inequality constraint (lower bound)
+     [ 0., -1.]])       # <-- LHS of inequality constraint (lower bound)
 
 b = np.array([0., 1., 1., 1., 1.])
 
-cones = [clarabel.ZeroConeT(1), clarabel.NonnegativeConeT(4)]
+cones = [
+    clarabel.ZeroConeT(1), 
+    clarabel.NonnegativeConeT(4)
+]
 
 settings = clarabel.DefaultSettings()