In this book, the author considers separable programming and, in
particular, one of its important cases - convex separable programming.
Some general results are presented, techniques of approximating the
separable problem by linear programming and dynamic programming are
considered.
Convex separable programs subject to inequality/ equality constraint(s)
and bounds on variables are also studied and iterative algorithms of
polynomial complexity are proposed.
As an application, these algorithms are used in the implementation of
stochastic quasigradient methods to some separable stochastic programs.
Numerical approximation with respect to I1 and I4
norms, as a convex separable nonsmooth unconstrained minimization
problem, is considered as well.
Audience: Advanced undergraduate and graduate students, mathematical
programming/ operations research specialists.