Commit 7664f262 by Sebastian Alexander Uerlich

### Fix stopping criterion in all Exchange ADMM algorithms

parent 51975719
 ... ... @@ -214,10 +214,7 @@ def exchange_admm(city_district, models=None, beta=1.0, eps_primal=0.1, # ------------------------------------------ # Calculate parameters for stopping criteria # ------------------------------------------ # TODO: Think about stopping criteria # From an interpretational perspective it would make sense to remove # the `n *` for the r norm and introduce a `1/n` factor for the s norm r_norms.append(n * np.linalg.norm(x_)) r_norms.append(np.math.sqrt(n) * np.linalg.norm(x_)) np.copyto( s[0:op_horizon], - rho * ( ... ...
 ... ... @@ -228,10 +228,7 @@ def exchange_admm_mpi(city_district, models=None, beta=1.0, eps_primal=0.1, # ------------------------------------------ # Calculate parameters for stopping criteria # ------------------------------------------ # TODO: Think about stopping criteria # From an interpretational perspective it would make sense to remove # the `n *` for the r norm and introduce a `1/n` factor for the s norm r_norms.append(n * np.linalg.norm(x_)) r_norms.append(np.math.sqrt(n) * np.linalg.norm(x_)) np.copyto( s[0:op_horizon], - rho * ( ... ...
 ... ... @@ -298,10 +298,7 @@ def exchange_admm_r_and_f_mpi(city_district, models=None, beta=1.0, eps_primal=0 # ------------------------------------------ # Calculate parameters for stopping criteria # ------------------------------------------ # TODO: Think about stopping criteria # From an interpretational perspective it would make sense to remove # the `n *` for the r norm and introduce a `1/n` factor for the s norm r_norms.append(n * np.linalg.norm(x_)) r_norms.append(np.math.sqrt(n) * np.linalg.norm(x_)) np.copyto( s[0:op_horizon], - rho * ( ... ...
 ... ... @@ -246,10 +246,7 @@ def exchange_admm_varying_mpi(city_district, models=None, beta=1.0, eps_primal=0 # ------------------------------------------ # Calculate parameters for stopping criteria # ------------------------------------------ # TODO: Think about stopping criteria # From an interpretational perspective it would make sense to remove # the `n *` for the r norm and introduce a `1/n` factor for the s norm r_norms.append(n * np.linalg.norm(x_)) r_norms.append(np.math.sqrt(n) * np.linalg.norm(x_)) np.copyto( s[0:op_horizon], - rho * ( ... ...
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