For a square matrix , the matrix exponential is defined bywhere is the identity matrix.
- [(1)] Let Compute the matrix exponential .
- [(2)] Consider the matrices where is a real number, and define the vector Let . Determine the minimum and maximum values of over all nonzero real vectors .
- [(3)] Let where is the matrix from part (2). Find the value of for the vector satisfying Let be the eigenvalues of the matrix in part {\rm (1)}. Let and be the minimum and maximum values, respectively, obtained in part {\rm (2)}, and letbe the value obtained in part {\rm (3)}. Compute\
[THOUGHT] Compute the required scalar using invariants: λ1λ2λ3 = det(A) and λ1^2+λ2^2+λ3^2 = trace(A^2). From parts (2)-(3), compute C explicitly via exp(A_x), get its minimum Rayleigh quotient m and the critical-point z, hence R^2. Then multiply det(A)mR^2*trace(A^2). Verify all with Python exactly. [/THOUGHT] [PYTHON] import sympy as sp
A = sp.Matrix([[-1,4,4],[-5,8,10],[3,-3,-5]]) detA = sp.factor(A.det()) traceA2 = sp.factor((A**2).trace())
detA, traceA2 print("det(A) =", detA) print("trace(A^2) =", traceA2)
Parts (2)-(3)
x = sp.symbols('x', real=True) c, s = sp.cos(x), sp.sin(x) expAx = sp.Matrix([[c,-s,0],[s,c,0],[0,0,sp.E]]) B = sp.diag(1,-1,1) C = sp.simplify(B*expAx) print("C =", C)
eigenvalues of C (should be -1, 1, e)
eigsC = sp.factor(C.eigenvals()) print("eigenvals(C) =", eigsC)
m = min([sp.N(ev) for ev in eigsC.keys()]) # numeric for min; but we know -1 print("numeric min eigenvalue =", m)
exact min:
m_exact = sp.Integer(-1)
a = sp.Matrix([1,1,sp.E]) z = sp.simplify(C.LUsolve(a)) # solve Cz=a R2 = sp.simplify((z.T*z)[0]) print("z =", z) print("R^2 =", R2)
expr = sp.simplify(detA * m_exact * R2 * traceA2) print("final expression =", expr) [/PYTHON]
[THOUGHT] Using the verified outputs: (\det(A)=-6), (\operatorname{tr}(A^2)=14), (m=-1), and (R^2=3). Thus the requested product is ((-6)(-1)(3)(14)=252). [ \boxed{252} ]