## Problem Description and Formulation

The problem is a linear programming (LP) optimization problem. The goal is to minimize the cost of a mixture of two bags, A and B, used to feed large animals in an aquarium. The mixture must contain at least 20 units of sardines and 25 units of anchovies.

## Decision Variables

Let \(x\) be the number of bags of type A and \(y\) be the number of bags of type B.

## Objective Function

The cost of bag A is $4 per bag, and the cost of bag B is $6 per bag. The objective is to minimize the total cost:
\[ \text{Minimize:} \quad 4x + 6y \]

## Constraints

1. **Sardine Constraint:** Each bag of A contains 2 units of sardines, and each bag of B contains 4 units of sardines. The mixture must contain at least 20 units of sardines:
\[ 2x + 4y \geq 20 \]

2. **Anchovy Constraint:** Each bag of A contains 3 units of anchovies, and each bag of B contains 1 unit of anchovies. The mixture must contain at least 25 units of anchovies:
\[ 3x + y \geq 25 \]

3. **Non-Negativity Constraints:** The number of bags cannot be negative:
\[ x \geq 0, \quad y \geq 0 \]

## Gurobi Code

```python
import gurobi

def solve_aquarium_feeding_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the decision variables
    x = model.addVar(lb=0, name="x")  # Number of bags of type A
    y = model.addVar(lb=0, name="y")  # Number of bags of type B

    # Define the objective function
    model.setObjective(4*x + 6*y, gurobi.GRB.MINIMIZE)

    # Add the sardine constraint
    model.addConstr(2*x + 4*y >= 20, name="sardine_constraint")

    # Add the anchovy constraint
    model.addConstr(3*x + y >= 25, name="anchovy_constraint")

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"x (Bags of A): {x.varValue}")
        print(f"y (Bags of B): {y.varValue}")
        print(f"Minimum Cost: ${model.objVal:.2f}")
    else:
        print("The model is infeasible or unbounded.")

solve_aquarium_feeding_problem()
```