Numerical recipes

10.8 Linear Programming and the Simplex Method 431
and simultaneously subject to M = m1 + m2 + m3 additional constraints, m1 of
them of the form
ai1x1 + ai2x2 + ···+ aiN xN ≤ bi (bi ≥ 0) i =1,...,m1 (10.8.3)
m2 of them of the form
aj1x1 + aj2x2 + ···+ ajNxN ≥ bj ≥ 0 j = m1 +1,...,m1 + m2 (10.8.4)
and m3 of them of the form
ak1x1 + ak2x2 + ···+ akN xN = bk ≥ 0
k = m1 + m2 +1,...,m1 + m2 + m3
(10.8.5)
The various aij’s can have either sign, or be zero. The fact that the b’s must all be
nonnegative (as indicated by the final inequality in the above three equations) is a
matter of convention only, since you can multiply any contrary inequality by −1.
There is no particular significance in the number of constraints M being less than,
equal to, or greater than the number of unknowns N.
A set of values x1 ...xN that satisfies the constraints (10.8.2)–(10.8.5) is called
a feasible vector. The function that we are trying to maximize is called the objective
function. The feasible vector that maximizes the objective function is called the
optimal feasible vector. An optimal feasible vector can fail to exist for two distinct
reasons: (i) there are no feasible vectors, i.e., the given constraints are incompatible,
or (ii) there is no maximum, i.e., there is a direction in N space where one or more
of the variables can be taken to infinity while still satisfying the constraints, giving
an unbounded value for the objective function.
As you see, the subject of linear programming is surrounded by notational and
terminological thickets. Both of these thorny defenses are lovingly cultivated by a
coterie of stern acolytes who have devoted themselves to the field. Actually, the
basic ideas of linear programming are quite simple. Avoiding the shrubbery, we
want to teach you the basics by means of a couple of specific examples; it should
then be quite obvious how to generalize.
Why is linear programming so important? (i) Because “nonnegativity” is the
usual constraint on any variable xi that represents the tangible amount of some
physical commodity, like guns, butter, dollars, units of vitamin E, food calories,
kilowatt hours, mass, etc. Hence equation (10.8.2). (ii) Because one is often
interested in additive (linear) limitations or bounds imposed by man or nature:
minimum nutritional requirement, maximum affordable cost, maximum on available
labor or capital, minimum tolerable level of voter approval, etc. Hence equations
(10.8.3)–(10.8.5). (iii) Because the function that one wants to optimize may be
linear, or else may at least be approximated by a linear function — since that is the
problem that linear programming can solve. Hence equation (10.8.1). For a short,
semipopular survey of linear programming applications, see Bland [1].
Here is a specific example of a problem in linear programming, which has
N =4, m1 =2, m2 = m3 =1, hence M =4:
Maximize z = x1 + x2 +3x3 − 1
2 x4
(10.8.6)
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