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csianglimcsianglim
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fix lp solver
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‎1. LP Problem - DMC.ipynb‎

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@@ -1219,28 +1219,61 @@
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"ax.relim()\n",
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"ax.autoscale_view()\n",
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"\n",
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"# # Solve the LP\n",
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"# def run_pulp(cost_MV1, cost_MV2, MV1_Lo=-limits, MV2_Lo=-limits):\n",
1224-
"# def run_lp(values):\n",
1225-
"# prob = LpProblem(\"DMC_problem\",LpMinimize)\n",
1226-
"# MV1=LpVariable(\"MV1\",-limits)\n",
1227-
"# MV2=LpVariable(\"MV2\",-limits)\n",
1228-
"# prob += values[2]*MV1 + values[3]*MV2, \"Cost function of MVs\"\n",
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"# Solve the LP\n",
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"def run_lp(values_dict):\n",
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" '''\n",
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" Run pulp solver for LP problem with the same gain matrix and CV limits as the example. Returns a tuple with:\n",
1226+
" (LP solution array [MV1_solution, MV2_solution], objective_value)\n",
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" ''' \n",
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" prob = LpProblem(\"DMC_problem\",LpMinimize)\n",
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" \n",
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" nMVs = 2\n",
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" nCVs = 2\n",
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" \n",
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" # How many MVs\n",
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" MVs = []\n",
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" for i in range(nMVs):\n",
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" MVs.append(LpVariable(f\"MV{i+1}\",-limits))\n",
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" \n",
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" # the objective function\n",
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" obj = 0\n",
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" for indx, MV in enumerate(MVs):\n",
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" obj += values_dict[f\"MV{indx+1} Cost\"]*MV\n",
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" \n",
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" prob += obj, \"Cost function of MVs\"\n",
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" \n",
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" # constraint formulation in terms of MV1 and MV2\n",
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" CV_contraint_lo = []\n",
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" CV_contraint_hi = []\n",
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" \n",
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" for i in range(nCVs):\n",
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" c = 0\n",
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" for indx, MV in enumerate(MVs):\n",
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" c += G[i][indx]*MV\n",
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" prob += c <= values_dict[f'CV{i+1} Limits'][1], f'CV{i+1} High Limit'\n",
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" prob += c >= values_dict[f'CV{i+1} Limits'][0], f'CV{i+1} Low Limit'\n",
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" \n",
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" for indx, MV in enumerate(MVs):\n",
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" prob += MV <= values_dict[f'MV{indx+1} Limits'][1], f'MV{indx+1} High Limit'\n",
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" prob += MV >= values_dict[f'MV{indx+1} Limits'][0], f'MV{indx+1} Low Limit' \n",
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" \n",
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"# # constraint formulation in terms of MV1 and MV2\n",
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"# prob += G11*MV1+G12*MV2 <= values[0][1], \"CV1 High Limit\"\n",
1232-
"# prob += G11*MV1+G12*MV2 >= values[0][0], \"CV1 Low Limit\"\n",
1233-
"# prob += G21*MV1+G22*MV2 <= values[1][1], \"CV2 High Limit\"\n",
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"# prob += G21*MV1+G22*MV2 >= values[1][0], \"CV2 Low Limit\"\n",
1235-
"# if (prob.solve(PULP_CBC_CMD(msg=0)) == 1):\n",
1236-
"# return [v.varValue for v in prob.variables()], value(prob.objective)\n",
1237-
"# else:\n",
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"# print(\"NOT SOLVED - Infeasibility!\")\n",
1239-
"# return [0,0], 0\n",
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" if (prob.solve(PULP_CBC_CMD(msg=0)) == 1):\n",
1261+
" return [v.varValue for v in prob.variables()], value(prob.objective)\n",
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" else:\n",
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" print(\"NOT SOLVED - Infeasibility!\")\n",
1264+
" return np.zeros(nMVs), 0\n",
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"\n",
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"def handle_slider_change(change):\n",
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" ## grab slider values for CV contraints\n",
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" values = [slider.value for slider in sliders]\n",
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" values_dict = {}\n",
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" values_dict['CV1 Limits'] = values[0]\n",
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" values_dict['CV2 Limits'] = values[1]\n",
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" values_dict['MV1 Limits'] = values[4]\n",
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" values_dict['MV2 Limits'] = values[5]\n",
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" values_dict['MV1 Cost'] = values[2]\n",
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" values_dict['MV2 Cost'] = values[3]\n",
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" \n",
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" cost_MV1 = values[2]\n",
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" cost_MV2 = values[3]\n",
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" \n",
@@ -1275,7 +1308,7 @@
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" q.set_UVC(-cost_MV1, -cost_MV2, z_obj)\n",
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" \n",
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" # Find the LP soln\n",
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" soln, V = run_pulp(cost_MV1, cost_MV2, MV1_Lo=values[4][0], MV1_Hi=values[4][1], MV2_Lo=values[5][0], MV2_Hi=values[5][1])\n",
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" soln, V = run_lp(values_dict)\n",
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" \n",
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" if (V == None):\n",
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" V = 0\n",
@@ -1405,7 +1438,7 @@
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},
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{
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"outputs": [],
@@ -1460,16 +1493,47 @@
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[0;31mNameError: \u001b[0mname 'n_frames' is not defined\n"
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]
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"text/plain": [
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"Button(description='Click to show stack trace', layout=Layout(height='auto', width='auto'), style=ButtonStyle(…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from matplotlib import animation\n",
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"anim = animation.FuncAnimation(fig, drawframe, frames=n_frames, interval=200)\n",
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"from IPython.display import HTML\n",
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"HTML(anim.to_jshtml())"
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]
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},
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{
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