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Solve the following python programming problem given the following code:
# Differential Evolution solver
from mystic.solvers import DifferentialEvolutionSolver2

# Chebyshev polynomial and cost function
from mystic.models.poly import chebyshev8, chebyshev8cost
from mystic.models.poly import chebyshev8coeffs

# tools
from mystic.termination import VTR, CollapseAt, Or
from mystic.strategy import Best1Exp
from mystic.monitors import VerboseMonitor
from mystic.tools import random_seed
from mystic.math import poly1d
import numpy as np

if __name__ == '__main__':

    print("Differential Evolution")
    print("======================")
    ndim = 9
    random_seed(123)

    # configure monitor
    stepmon = VerboseMonitor(50,50)

    # build a constraints function
    def constraints(x):
        x[-1] = 1.
        return np.round(x)

    stop = Or(VTR(0.0001), CollapseAt(0.0, generations=2))

    # use DE to solve 8th-order Chebyshev coefficients
    npop = 10*ndim
    solver = DifferentialEvolutionSolver2(ndim,npop)
    solver.SetRandomInitialPoints(min=[-100]*ndim, max=[100]*ndim)
    solver.SetGenerationMonitor(stepmon)
    solver.SetConstraints(constraints)
    solver.enable_signal_handler()
    solver.Solve(chebyshev8cost, termination=stop, strategy=Best1Exp, \
                 CrossProbability=1.0, ScalingFactor=0.9)
    solution = solver.Solution()

    # use monitor to retrieve results information
    iterations = len(stepmon)
    cost = stepmon.y[-1]
    print("Generation %d has best Chi-Squared: %f" % (iterations, cost))

    # use pretty print for polynomials
    print(poly1d(solution))

    # compare solution with actual 8th-order Chebyshev coefficients
    print("\nActual Coefficients:\n %s\n" % poly1d(chebyshev8coeffs))
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python code to print for polynomials
.

EXERCISE: Solve the chebyshev8.cost example exactly, by applying the knowledge that the last term in the chebyshev polynomial will always be be one. Use numpy.round or mystic.constraints.integers or to constrain solutions to the set of integers.  Does using mystic.suppressed to supress small numbers accelerate the solution?