latqcdtools.math.optimize
=============

`minimize(func, jac=None, hess=None, start_params=None, tol=1e-12, maxiter=10000, algorithm=None)`
 
    Wrapper for scipy.optimize.minimize. Helps that all algorithms have common syntax.

    Args:
        func: to-be-minimized function 
        jac (func, optional): Explicit Jacobian. Defaults to None.
        hess (func, optional): Explicit Hessian. Defaults to None.
        start_params (array-like, optional): Starting guess. Defaults to None.
        tol (float, optional): Solve tolerance. Defaults to 1e-12.
        maxiter (int, optional): Maximum number of solve iterations. Defaults to 10000.
        algorithm (str, optional): Solve algorithm. Defaults to Scipy default, usually BFGS.

    Raises:
        e: scipy.optimize exception 
        ValueError: minimizer fails to converge

    Returns:
        array-like: solution vector 
    
`persistentSolve(LHS, guess, tol=1e-08, maxiter=300)`
 
    Attempt to solve LHS==0 using, in this order, SciPy's newton_krylov, fsolve, and root.

    Args:
        LHS (func)
        guess: initial guess for solution 
        tol (real, optional): Solve tolerance. Defaults to 1e-8.
        maxiter (int, optional): Maximum iterations. Defaults to 300.
    
`solve(LHS, guess, tol=1e-08, maxiter=300, method='newton_krylov')`
 
    Wrapper for various methods to solve LHS==0. This is to simplify the interface.

    Args:
        LHS (func)
        guess: initial guess for solution 
        tol (real, optional): Solve tolerance. Defaults to 1e-8.
        maxiter (int, optional): Maximum iterations. Defaults to 300.
        method (str, optional): Defaults to 'newton_krylov'.