z

gap.json

@@ -0,0 +1,56 @@
+{
+    "SolutionInfo": {
+        "Status": 2,
+        "Runtime": 1.2390613555908203e-03,
+        "Work": 1.0477234319444459e-04,
+        "ObjVal": 978.02,
+        "ObjBound": 978.02,
+        "ObjBoundC": 978.02,
+        "MIPGap": 0,
+        "IntVio": 0,
+        "BoundVio": 0,
+        "ConstrVio": 1.2434497875801753e-14,
+        "IterCount": 9,
+        "BarIterCount": 0,
+        "NodeCount": 1,
+        "SolCount": 1,
+        "PoolObjBound": 978.02,
+        "PoolObjVal": [
+            978.02
+        ]
+    },
+    "Vars": [
+        {
+            "VarName": "l",
+            "X": 9.99
+        },
+        {
+            "VarName": "b",
+            "X": 100
+        },
+        {
+            "VarName": "w1",
+            "X": 999
+        },
+        {
+            "VarName": "w2",
+            "X": 1000
+        },
+        {
+            "VarName": "w3",
+            "X": 1
+        },
+        {
+            "VarName": "c1",
+            "X": 99
+        },
+        {
+            "VarName": "c2",
+            "X": 1
+        },
+        {
+            "VarName": "c3",
+            "X": 101
+        }
+    ]
+}

gap.py

@@ -0,0 +1,48 @@
+import gurobipy as gp
+from gurobipy import GRB
+
+def solve_lp():
+    # 1. Create model
+    model = gp.Model("my_lp")
+
+    # 2. Add variables
+    # Example: x, y >= 0
+    l = model.addVar(lb=0, ub= 100, name="l", vtype=GRB.CONTINUOUS)
+    b = model.addVar(lb=0, ub= 100, name="b", vtype=GRB.INTEGER)
+    w = {}
+    c = {}
+    for i in range(1, 4):
+        w[i] = model.addVar(lb=1, ub=1000, name=f"w{i}", vtype= GRB.INTEGER)
+    for i in range(1, 4):
+        c[i] = model.addVar(lb=1, ub=1000, name=f"c{i}", vtype= GRB.INTEGER)
+    ans = model.addVar(lb=0, name="ans", vtype=GRB.CONTINUOUS)
+
+    # 3. Set objective
+    # model.setObjective(ans, GRB.MAXIMIZE)
+    model.setObjective(l*(c[1]-c[2])-w[2]+w[1], GRB.MAXIMIZE)
+
+    # 4. Add constraints
+    # model.addConstr(w[2] >= w[1] >= w[3] >= 0)
+    model.addConstr(w[2] >= 0.1+ w[1], name="w2_ge_w1")
+    model.addConstr(w[1] >= 0.1+ w[3], name="w1_ge_w3")
+    model.addConstr(w[3] >= 0.1+ 0,     name="w3_ge_0")
+    # model.addConstr(c[3] >= b >= c[1] >= c[2] >= 0)
+    model.addConstr(c[3] >= 0.1+ b,     name="c3_ge_b")
+    model.addConstr(b    >= 0.1+ c[1],  name="b_ge_c1")
+    model.addConstr(c[1] >= 0.1+ c[2],  name="c1_ge_c2")
+    model.addConstr(c[2] >= 0.1+ 0,     name="c2_ge_0")
+    model.addConstr((c[3]-c[2])*l >= w[2]-w[3])
+    model.addConstr((c[3]-c[2])*l <= w[2]-w[3])
+    # model.addConstr(ans*b <= l*(c[1]-c[2])-w[2]+w[1])
+
+    # 5. Optimize
+    model.optimize()
+
+    # 6. Retrieve results
+    if model.status == GRB.OPTIMAL:
+        model.write("gap.json")
+    else:
+        print("No optimal solution found.")
+
+if __name__ == "__main__":
+    solve_lp()