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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.
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