## Problem Description and Symbolic Representation

The problem requires maximizing the return on investment for a client with $60,000 to invest over a 1-year term. The client can invest in a trust yielding a 2% return or a savings account yielding a 3% return. The conditions are that at least 15% of the investment must be placed in the trust and at most 80% of the investment can be placed in the savings account.

### Symbolic Representation

Let's denote:
- \(x_1\) as the amount invested in the trust,
- \(x_2\) as the amount invested in the savings account.

The objective is to maximize the total return, which can be represented as \(0.02x_1 + 0.03x_2\).

The constraints based on the problem description are:
1. \(x_1 + x_2 = 60,000\) (total investment),
2. \(x_1 \geq 0.15 \times 60,000\) (at least 15% in the trust),
3. \(x_2 \leq 0.80 \times 60,000\) (at most 80% in the savings account),
4. \(x_1, x_2 \geq 0\) (non-negativity constraint).

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'amount invested in the trust'), ('x2', 'amount invested in the savings account')],
    'objective_function': '0.02*x1 + 0.03*x2',
    'constraints': [
        'x1 + x2 = 60000',
        'x1 >= 0.15 * 60000',
        'x2 <= 0.80 * 60000',
        'x1, x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name='trust_investment', lb=0)
    x2 = model.addVar(name='savings_investment', lb=0)

    # Objective function: maximize 0.02*x1 + 0.03*x2
    model.setObjective(0.02 * x1 + 0.03 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 == 60000, name='total_investment')
    model.addConstr(x1 >= 0.15 * 60000, name='min_trust_investment')
    model.addConstr(x2 <= 0.80 * 60000, name='max_savings_investment')

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal investment in trust: ${x1.varValue:.2f}")
        print(f"Optimal investment in savings: ${x2.varValue:.2f}")
        print(f"Max return: ${0.02*x1.varValue + 0.03*x2.varValue:.2f}")
    else:
        print("No optimal solution found.")

solve_investment_problem()
```