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In mathematical optimization, fractional programming is a generalization of linear-fractional programming. The objective function in a fractional program is a ratio of two functions that are in general nonlinear. The ratio to be optimized often describes some kind of efficiency of a system.
In mathematical optimization, linear-fractional programming (LFP) is a generalization of linear programming (LP). Whereas the objective function in a linear program is a linear function, the objective function in a linear-fractional program is a ratio of two linear functions. A linear program can be regarded as a special case of a linear ...
Fractional numbers are supported on most programming languages as floating-point numbers or fixed-point numbers. However, such representations typically restrict the denominator to a power of two. Most decimal fractions (or most fractions in general) cannot be represented exactly as a fraction with a denominator that is a power of two.
2-a. Let be an instance constructed from by grouping items and rounding the size of items in each group to the highest item in the group. 3-a. Construct the configuration linear program for , without the integrality constraints. 4. Compute a (fractional) solution x for the relaxed linear program. 3-b.
Python: The standard library includes a Fraction class in the module fractions. [6] Ruby: native support using special syntax. Smalltalk represents rational numbers using a Fraction class in the form p/q where p and q are arbitrary size integers. Applying the arithmetic operations *, +, -, /, to fractions returns a reduced fraction. With ...
The fractional configuration LP of bin-packing It is the linear programming relaxation of the above ILP. It replaces the last constraint {, …,} with the constraint .In other words, each configuration can be used a fractional number of times.
Some computer languages have implementations of decimal floating-point arithmetic, including PL/I, .NET, [3] emacs with calc, and Python's decimal module. [4] In 1987, the IEEE released IEEE 854 , a standard for computing with decimal floating point, which lacked a specification for how floating-point data should be encoded for interchange with ...
For the set cover problem, Lovász proved that the integrality gap for an instance with n elements is H n, the nth harmonic number. One can turn the linear programming relaxation for this problem into an approximate solution of the original unrelaxed set cover instance via the technique of randomized rounding. [2]