Justification:

If a linear programming (LP) feasible region is not unbounded, then one can say that the LP’s feasible region is bounded.

If the feasible region is bounded or feasible region is closed, the number of its extreme points or geometrically the number of vertices of a hyper polyhedron is a multidimensional space is always finite.

A LP function attains its optimal values either in a single extreme point or a values combination of extreme points.

So, simply checking the z-values at each of the feasible region’s extreme points. If region is unbounded, the objective function may not attain its optimal at all, or the region may have no feasible extreme points.

If the feasible region is bounded, but not unbounded or close, all values are bounded...

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Chapter 3.3, Problem 6P

Chapter 3.3, Problem 8P

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Ch. 3.2 - Prob. 6P Ch. 3.3 - Prob. 1P Ch. 3.3 - Prob. 2P Ch. 3.3 - Prob. 3P Ch. 3.3 - Prob. 4P Ch. 3.3 - Prob. 5P Ch. 3.3 - Prob. 6P Ch. 3.3 - Prob. 7P Ch. 3.3 - Prob. 8P Ch. 3.3 - Prob. 9P Ch. 3.3 - Prob. 10P Ch. 3.4 - Prob. 1P Ch. 3.4 - Prob. 2P Ch. 3.4 - Prob. 3P Ch. 3.4 - Prob. 4P Ch. 3.5 - Prob. 1P Ch. 3.5 - Prob. 2P Ch. 3.5 - Prob. 3P Ch. 3.5 - Prob. 4P Ch. 3.5 - Prob. 5P Ch. 3.5 - Prob. 6P Ch. 3.5 - Prob. 7P Ch. 3.6 - Prob. 1P Ch. 3.6 - Prob. 2P Ch. 3.6 - Prob. 3P Ch. 3.6 - Prob. 4P Ch. 3.6 - Prob. 5P Ch. 3.7 - Prob. 1P Ch. 3.8 - Prob. 1P Ch. 3.8 - Prob. 2P Ch. 3.8 - Prob. 3P Ch. 3.8 - Prob. 4P Ch. 3.8 - Prob. 5P Ch. 3.8 - Prob. 6P Ch. 3.8 - Prob. 7P Ch. 3.8 - Prob. 8P Ch. 3.8 - Prob. 9P Ch. 3.8 - Prob. 10P Ch. 3.8 - Prob. 11P Ch. 3.8 - Prob. 12P Ch. 3.8 - Prob. 13P Ch. 3.8 - Prob. 14P Ch. 3.9 - Prob. 1P Ch. 3.9 - Prob. 2P Ch. 3.9 - Prob. 3P Ch. 3.9 - Prob. 4P Ch. 3.9 - Prob. 5P Ch. 3.9 - Prob. 6P Ch. 3.9 - Prob. 7P Ch. 3.9 - Prob. 8P Ch. 3.9 - Prob. 9P Ch. 3.9 - Prob. 10P Ch. 3.9 - Prob. 11P Ch. 3.9 - Prob. 12P Ch. 3.9 - Prob. 13P Ch. 3.9 - Prob. 14P Ch. 3.10 - Prob. 1P Ch. 3.10 - Prob. 2P Ch. 3.10 - Prob. 3P Ch. 3.10 - Prob. 4P Ch. 3.10 - Prob. 5P Ch. 3.10 - Prob. 6P Ch. 3.10 - Prob. 7P Ch. 3.10 - Prob. 8P Ch. 3.10 - Prob. 9P Ch. 3.11 - Prob. 1P Ch. 3.11 - Show that Fincos objective function may also be...Ch. 3.11 - Prob. 3P Ch. 3.11 - Prob. 4P Ch. 3.11 - Prob. 7P Ch. 3.11 - Prob. 8P Ch. 3.11 - Prob. 9P Ch. 3.12 - Prob. 2P Ch. 3.12 - Prob. 3P Ch. 3.12 - Prob. 4P Ch. 3 - Prob. 1RP Ch. 3 - Prob. 2RP Ch. 3 - Prob. 3RP Ch. 3 - Prob. 4RP Ch. 3 - Prob. 5RP Ch. 3 - Prob. 6RP Ch. 3 - Prob. 7RP Ch. 3 - Prob. 8RP Ch. 3 - Prob. 9RP Ch. 3 - Prob. 10RP Ch. 3 - Prob. 11RP Ch. 3 - Prob. 12RP Ch. 3 - Prob. 13RP Ch. 3 - Prob. 14RP Ch. 3 - Prob. 15RP Ch. 3 - Prob. 16RP Ch. 3 - Prob. 17RP Ch. 3 - Prob. 18RP Ch. 3 - Prob. 19RP Ch. 3 - Prob. 20RP Ch. 3 - Prob. 21RP Ch. 3 - Prob. 22RP Ch. 3 - Prob. 23RP Ch. 3 - Prob. 24RP Ch. 3 - Prob. 25RP Ch. 3 - Prob. 26RP Ch. 3 - Prob. 27RP Ch. 3 - Prob. 28RP Ch. 3 - Prob. 29RP Ch. 3 - Prob. 30RP Ch. 3 - Prob. 31RP Ch. 3 - Prob. 32RP Ch. 3 - Prob. 33RP Ch. 3 - Prob. 34RP Ch. 3 - Prob. 35RP Ch. 3 - Prob. 36RP Ch. 3 - Prob. 37RP Ch. 3 - Prob. 38RP Ch. 3 - Prob. 39RP Ch. 3 - Prob. 40RP Ch. 3 - Prob. 41RP Ch. 3 - Prob. 42RP Ch. 3 - Prob. 43RP Ch. 3 - Prob. 44RP Ch. 3 - Prob. 45RP Ch. 3 - Prob. 46RP Ch. 3 - Prob. 47RP Ch. 3 - Prob. 48RP Ch. 3 - Prob. 49RP Ch. 3 - Prob. 50RP Ch. 3 - Prob. 51RP Ch. 3 - Prob. 52RP Ch. 3 - Prob. 53RP Ch. 3 - Prob. 54RP Ch. 3 - Prob. 56RP Ch. 3 - Prob. 57RP Ch. 3 - Prob. 58RP Ch. 3 - Prob. 59RP Ch. 3 - Prob. 60RP Ch. 3 - Prob. 61RP Ch. 3 - Prob. 62RP Ch. 3 - Prob. 63RP

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Solution Summary: The author explains that if a linear programming (LP) feasible region is not unbounded, then one can say that the LP’s feasible area is bended.

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