  This paper is concerned with a stochastic linear quadratic (LQ, for short)
optimal control problem. The notions of open-loop and closed-loop solvabilities
are introduced. A simple example shows that these two solvabilities are
different. Closed-loop solvability is established by means of solvability of
the corresponding Riccati equation, which is implied by the uniform convexity
of the quadratic cost functional. Conditions ensuring the convexity of the cost
functional are discussed, including the issue that how negative the control
weighting matrix-valued function R(s) can be. Finiteness of the LQ problem is
characterized by the convergence of the solutions to a family of Riccati
equations. Then, a minimizing sequence, whose convergence is equivalent to the
open-loop solvability of the problem, is constructed. Finally, an illustrative
example is presented.
