Bug fixed: to_lp, cpt_solver, and soc_solve.

README.md

@@ -1,4 +1,4 @@
-<img src="https://github.com/XiongPengNUS/rsome/blob/master/rsologo.png?raw=true" width=100>
+<img src="https://github.com/XiongPengNUS/rsome/blob/master/rsologo.png?raw=true" width=200>
 
 # Robust Stochastic Optimization Made Easy
 
@@ -13,7 +13,7 @@
 ![GitHub issues](https://img.shields.io/github/issues-raw/XiongPengNUS/rsome)
 
 - Website: [RSOME for Python](https://xiongpengnus.github.io/rsome/)
-- PyPI: [RSOME 1.2.5](https://pypi.org/project/rsome/)
+- PyPI: [RSOME 1.2.6](https://pypi.org/project/rsome/)
 
 RSOME (Robust Stochastic Optimization Made Easy) is an open-source Python package for generic modeling of optimization problems (subject to uncertainty). Models in RSOME are constructed by variables, constraints, and expressions that are formatted as N-dimensional arrays. These arrays are consistent with the NumPy library in terms of syntax and operations, including broadcasting, indexing, slicing, element-wise operations, and matrix calculation rules, among others. In short, RSOME provides a convenient platform to facilitate developments of robust optimization models and their applications.
 

rsome/cpt_solver.py

@@ -34,9 +34,7 @@ def solve(form, display=True, log=False, params={}):
 
     try:
         if xmat:
-            warnings.warn('The SOCP solver ignores exponential cone constraints. ')
-        # if lmi:
-        #     warnings.warn('The SOCP solver ignores semidefinite cone constraints. ')
+            warnings.warn('The conic solver ignores exponential cone constraints. ')
     except AttributeError:
         pass
 
@@ -52,14 +50,15 @@ def solve(form, display=True, log=False, params={}):
     linear_ineq = form.linear[~is_eq]
     linear_eq = form.linear[is_eq]
     num_ineq = linear_ineq.shape[0]
-    # num_eq = linear_eq.shape[0]
 
     const_ineq = form.const[~is_eq]
     const_eq = form.const[is_eq]
 
     num_constr, num_var = form.linear.shape
 
-    env = cp.Envr()
+    envconfig = cp.EnvrConfig()
+    envconfig.set('nobanner', '1')
+    env = cp.Envr(envconfig)
     m = env.createModel()
     m.setParam(cp.COPT.Param.Logging, log)
     m.setParam(cp.COPT.Param.LogToConsole, False)
@@ -149,7 +148,14 @@ def solve(form, display=True, log=False, params={}):
         else:
             y = None
 
-        solution = Solution('COPT', objval, x_sol, status, stime, y=y)
+        xx_sol = np.zeros(len(form.vtype))
+        xx_sol[idx_cont] = x_sol[:len(idx_cont)]
+        if idx_bin:
+            xx_sol[idx_bin] = x_sol[len(idx_cont): len(idx_cont)+len(idx_bin)]
+        if idx_int:
+            xx_sol[idx_int] = x_sol[len(idx_cont)+len(idx_bin):]
+
+        solution = Solution('COPT', objval, xx_sol, status, stime, y=y)
     except cp.CoptError:
         warnings.warn('Fail to find the optimal solution.')
         # solution = None

rsome/gcp.py

@@ -543,6 +543,7 @@ def to_socp(self, degree=4, cuts=(-30, 60)):
 
         left_width = self.linear.shape[1]
         qmat = self.qmat
+        lmi = self.lmi
 
         linear = self.linear
         const = self.const
@@ -570,4 +571,5 @@ def to_socp(self, degree=4, cuts=(-30, 60)):
             qmat += [list(left_width + num_vars + np.array([2, 1, 0]) + q*3)
                      for q in range(3+degree)]
 
-        return SOCProg(linear, const, sense, vtype, ub, lb, qmat, obj)
+        # return SOCProg(linear, const, sense, vtype, ub, lb, qmat, obj)
+        return GCProg(linear, const, sense, vtype, ub, lb, qmat, [], lmi, obj)