## Problem Description and Formulation

The problem is an optimization problem with two variables: 'intrusion analysts' and 'honeypots'. The goal is to minimize the objective function: $6.44 \times \text{intrusion analysts} + 9.58 \times \text{honeypots}$, subject to several constraints.

### Constraints

1. Power consumption constraints:
   - Intrusion analysts consume 5 kWh of power.
   - Honeypots consume 18 kWh of power.
   - Total power consumption must be at least 40 kWh.
   - Total power consumption must not exceed 88 kWh.

2. Computational load constraints:
   - Intrusion analysts contribute 4 TFLOPs.
   - Honeypots contribute 8 TFLOPs.
   - Total computational load must be at least 13 TFLOPs.
   - Total computational load must not exceed 77 TFLOPs.

3. Additional constraints:
   - $6 \times \text{intrusion analysts} - 4 \times \text{honeypots} \geq 0$.
   - Intrusion analysts and honeypots must be non-negative integers.

## Gurobi Code Formulation

```python
import gurobi as gp

# Create a new model
m = gp.Model("intrusion_analysts_honeypots")

# Define the variables
intrusion_analysts = m.addVar(name="intrusion_analysts", vtype=gp.GRB.INTEGER)
honeypots = m.addVar(name="honeypots", vtype=gp.GRB.INTEGER)

# Objective function
m.setObjective(6.44 * intrusion_analysts + 9.58 * honeypots, gp.GRB.MINIMIZE)

# Constraints
m.addConstr(5 * intrusion_analysts + 18 * honeypots >= 40, name="power_min")
m.addConstr(5 * intrusion_analysts + 18 * honeypots <= 88, name="power_max")
m.addConstr(4 * intrusion_analysts + 8 * honeypots >= 13, name="computational_load_min")
m.addConstr(4 * intrusion_analysts + 8 * honeypots <= 77, name="computational_load_max")
m.addConstr(6 * intrusion_analysts - 4 * honeypots >= 0, name="additional_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Intrusion Analysts: {intrusion_analysts.varValue}")
    print(f"Honeypots: {honeypots.varValue}")
    print(f"Objective: {m.objVal}")
else:
    print("No optimal solution found")
```