Math Computation

Mathematical Computation

Symbolic and numerical mathematics. Venv: source /Users/zhangmingda/clawd/.venv/bin/activate

Symbolic Math (SymPy)

from sympy import *
x, y, z, t = symbols('x y z t')
a, b, c = symbols('a b c', real=True)
n, k = symbols('n k', integer=True, positive=True)

# Solve equations
solve(x**2 - 5*x + 6, x)  # [2, 3]
solve([x + y - 5, x - y - 1], [x, y])  # {x: 3, y: 2}

# Calculus
diff(sin(x)*exp(x), x)           # derivative
integrate(x**2 * exp(-x), (x, 0, oo))  # definite integral
limit(sin(x)/x, x, 0)            # limit
series(exp(x), x, 0, 5)          # Taylor series

# Linear algebra
M = Matrix([[1, 2], [3, 4]])
M.eigenvals()    # eigenvalues
M.eigenvects()   # eigenvectors
M.det()          # determinant
M.inv()          # inverse

# Differential equations
f = Function('f')
dsolve(f(x).diff(x, 2) + f(x), f(x))  # y'' + y = 0

# Simplification
simplify(sin(x)**2 + cos(x)**2)  # 1
trigsimp(expr)
factor(expr)
expand(expr)

# LaTeX output
latex(expr)  # for paper-ready equations

Numerical Methods (SciPy)

from scipy import optimize, integrate, linalg, interpolate
import numpy as np

# Root finding
root = optimize.brentq(lambda x: x**3 - 2*x - 5, 2, 3)

# Optimization
result = optimize.minimize(lambda x: (x[0]-1)**2 + (x[1]-2.5)**2,
                          x0=[0, 0], method='Nelder-Mead')

# Constrained optimization
from scipy.optimize import linprog, minimize
result = minimize(objective, x0, constraints=constraints, bounds=bounds)

# Numerical integration
val, err = integrate.quad(lambda x: np.exp(-x**2), -np.inf, np.inf)  # √π

# ODE solving
from scipy.integrate import solve_ivp
def lorenz(t, state, sigma=10, rho=28, beta=8/3):
    x, y, z = state
    return [sigma*(y-x), x*(rho-z)-y, x*y-beta*z]
sol = solve_ivp(lorenz, [0, 50], [1, 1, 1], dense_output=True, max_step=0.01)

# Interpolation
f_interp = interpolate.interp1d(x_data, y_data, kind='cubic')

# FFT
from scipy.fft import fft, fftfreq
yf = fft(signal)
xf = fftfreq(N, 1/sample_rate)

Linear Algebra

# NumPy
A = np.array([[1, 2], [3, 4]])
np.linalg.eig(A)        # eigendecomposition
np.linalg.svd(A)        # SVD
np.linalg.solve(A, b)   # solve Ax = b
np.linalg.norm(A)       # matrix norm
np.linalg.matrix_rank(A)

# Sparse matrices (SciPy)
from scipy.sparse import csr_matrix, linalg as sparse_linalg

Mathematical Modeling Workflow

1. Define the system and variables 2. Formulate equations (conservation laws, constitutive relations) 3. Non-dimensionalize if appropriate 4. Solve analytically (SymPy) or numerically (SciPy) 5. Validate against known solutions or data 6. Sensitivity analysis on parameters 7. Visualize results

Common Models

• Population dynamics: Lotka-Volterra, SIR/SEIR epidemiological
• Diffusion: Heat equation, Fick's law
• Mechanics: Newton's laws, Lagrangian/Hamiltonian
• Economics: Supply-demand, game theory, optimal control
• Networks: Graph theory, flow optimization

Tips

• Use SymPy for exact solutions, SciPy for numerical
• Always verify numerical solutions against analytical when possible
• Check units and dimensional consistency
• Use latex() to generate paper-ready equations
• For large systems, consider sparse matrix methods