Integer Programming: Theory and Practice by John K. Karlof

By John K. Karlof

Integer Programming: thought and perform includes refereed articles that discover either theoretical elements of integer programming in addition to significant functions. This quantity starts off with an outline of recent positive and iterative seek equipment for fixing the Boolean optimization challenge (BOOP). Following a evaluation of modern advancements on convergent Lagrangian options that use goal level-cut and domain-cut the way to remedy separable nonlinear integer-programming difficulties, the booklet discusses the generalized project challenge (GAP). the ultimate theoretical bankruptcy analyzes using decomposition tips on how to receive bounds at the optimum price of suggestions to integer linear-programming difficulties. the 1st software article comprises types and resolution algorithms for the rescheduling of airways following the transitority closure of airports. the subsequent chapters care for the choice of an optimum mixture of chartered and self-owned vessels had to delivery a product. The publication then offers an program of integer programming that comprises the seize, garage, and transmission of enormous amounts of information accumulated in the course of checking out eventualities regarding army functions relating to automobiles, medication, apparatus, missiles, and plane. the following article develops an integer linear-programming version to figure out the collection of goods that needs to be carried by way of shops inside of a retail chain to maximise revenue, and the ultimate article comprises an outline of noncommercial software program instruments for the answer of mixed-integer linear courses (MILP). The authors purposefully contain purposes and conception which are often no longer present in contributed books to be able to attract a wide selection of researchers and practitioners.

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Integer Programming: Theory and Practice

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Let u be an upper bound of f ∗ . We denote u − d(λ∗ ) as a duality bound between (P) and (D). It is clear that a duality bound is always larger than or equal to the duality gap. If x ∗ solves (L λ∗ ) with λ∗ ≥ 0, and, in addition, the following conditions are satisfied: gi (x ∗ ) ≤ b i , i = 1, 2, . . , m, λi∗ (gi (x ∗ ) − b i ) = 0, i = 1, 2, . . , the duality gap is zero. In this situation, the strong Lagrangian duality condition is said to be satisfied. Unfortunately, the strong Lagrangian duality is rarely present in integer programming, and a nonzero duality gap often exists when the Lagrangian relaxation method is adopted.

I=1 We call α, β an integer box. Let l = (l1 , . . , ln )T and u = (u1 , . . , un )T . Assume that the integer set X in (P) is given by X = l, u . If the objective function is nonincreasing and constraints are nondecreasing, we have the following conclusions: i. If x ∈ l, u is a feasible solution to (P), then for any x˜ ∈ l, x , it holds that f (x˜ ) ≥ f (x). ii. If y ∈ l, u is an infeasible solution to (P), then any point in y, u is infeasible. Therefore, l, x and y, u can be cut from the l, u , without missing any optimal solution of (P) after recording the feasible solution x.

Our outcomes are only slightly worse than those of the special purpose DML method of Shang [17], although we are undertaking to solve the much larger transformed problem and make no use of any specialization. 6 Performance Profiles It is always very difficult to compare different methods based on tables of computational results, unless one method is best on all the tests. We therefore also compare our methods using the ideas given in Dolan and Mor´e [3]. Based on the time used to find the best solution, we can construct a performance profile as follows.

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