Linear-fractional programming

Both linear programming and linear-fractional programming represent optimization problems using linear equations and linear inequalities, which for each problem-instance define a feasible set. Fractional linear programs have a richer set of objective functions. Informally, linear programming computes a policy delivering the best outcome, such as maximum profit or lowest cost. In contrast, a linear-fractional programming is used to achieve the highest ratio of outcome to cost, the ratio representing the highest efficiency. For example, in the context of LP we maximize the objective function profit = income − cost and might obtain maximum profit of \$100 (= \$1100 of income − \$1000 of cost). Thus, in LP we have an efficiency of \$100/\$1000 = 0.1. Using LFP we might obtain an efficiency of \$10/\$50 = 0.2 with a profit of only \$10, but only requiring \$50 of investment.

Formally, a linear-fractional program is defined as the problem of maximizing (or minimizing) a ratio of affine functions over a polyhedron,

${\displaystyle {\begin{aligned}{\text{maximize}}\quad &{\frac {\mathbf {c} ^{T}\mathbf {x} +\alpha }{\mathbf {d} ^{T}\mathbf {x} +\beta }}\\{\text{subject to}}\quad &A\mathbf {x} \leq \mathbf {b} ,\end{aligned}}}$

where ${\displaystyle \mathbf {x} \in \mathbb {R} ^{n}}$ represents the vector of variables to be determined, ${\displaystyle \mathbf {c} ,\mathbf {d} \in \mathbb {R} ^{n}}$ and ${\displaystyle \mathbf {b} \in \mathbb {R} ^{m}}$ are vectors of (known) coefficients, ${\displaystyle A\in \mathbb {R} ^{m\times n}}$ is a (known) matrix of coefficients and ${\displaystyle \alpha ,\beta \in \mathbb {R} }$ are constants. The constraints have to restrict the feasible region to ${\displaystyle \{\mathbf {x} |\mathbf {d} ^{T}\mathbf {x} +\beta >0\}}$, i.e. the region on which the denominator is positive.[1][2] Alternatively, the denominator of the objective function has to be strictly negative in the entire feasible region.

Under the assumption that the feasible region is non-empty and bounded, the Charnes-Cooper transformation[1]

${\displaystyle \mathbf {y} ={\frac {1}{\mathbf {d} ^{T}\mathbf {x} +\beta }}\cdot \mathbf {x} \;;\;\;t={\frac {1}{\mathbf {d} ^{T}\mathbf {x} +\beta }}}$

translates the linear-fractional program above to the equivalent linear program:

${\displaystyle {\begin{aligned}{\text{maximize}}\quad &\mathbf {c} ^{T}\mathbf {y} +\alpha t\\{\text{subject to}}\quad &A\mathbf {y} \leq \mathbf {b} t\\&\mathbf {d} ^{T}\mathbf {y} +\beta t=1\\&t\geq 0.\end{aligned}}}$

Then the solution for ${\displaystyle \mathbf {y} }$ and ${\displaystyle t}$ yields the solution of the original problem as

${\displaystyle \mathbf {x} ={\frac {1}{t}}\mathbf {y} .}$

Let the dual variables associated with the constraints ${\displaystyle A\mathbf {y} -\mathbf {b} t\leq \mathbf {0} }$ and ${\displaystyle \mathbf {d} ^{T}\mathbf {y} +\beta t-1=0}$ be denoted by ${\displaystyle \mathbf {u} }$ and ${\displaystyle \lambda }$, respectively. Then the dual of the LFP above is [3][4]

${\displaystyle {\begin{aligned}{\text{minimize}}\quad &\lambda \\{\text{subject to}}\quad &A^{T}\mathbf {u} +\lambda \mathbf {d} =\mathbf {c} \\&-\mathbf {b} ^{T}\mathbf {u} +\lambda \beta \geq \alpha \\&\mathbf {u} \in \mathbb {R} _{+}^{m},\lambda \in \mathbb {R} ,\end{aligned}}}$

which is an LP and which coincides with the dual of the equivalent linear program resulting from the Charnes-Cooper transformation.

The objective function in a linear-fractional problem is both quasiconcave and quasiconvex (hence quasilinear) with a monotone property, pseudoconvexity, which is a stronger property than quasiconvexity. A linear-fractional objective function is both pseudoconvex and pseudoconcave, hence pseudolinear. Since an LFP can be transformed to an LP, it can be solved using any LP solution method, such as the simplex algorithm (of George B. Dantzig),[5][6][7][8] the criss-cross algorithm,[9] or interior-point methods.

• Craven, B. D. (1988). Fractional programming. Sigma Series in Applied Mathematics. 4. Berlin: Heldermann Verlag. p. 145. ISBN 978-3-88538-404-5. MR 0949209.
• Illés, Tibor; Szirmai, Ákos; Terlaky, Tamás (1999). "The finite criss-cross method for hyperbolic programming". European Journal of Operational Research. 114 (1): 198–214. CiteSeerX 10.1.1.36.7090. doi:10.1016/S0377-2217(98)00049-6. Zbl 0953.90055. Postscript preprint.CS1 maint: ref=harv (link)
• Kruk, Serge; Wolkowicz, Henry (1999). "Pseudolinear programming". SIAM Review. 41 (4): 795–805. doi:10.1137/S0036144598335259. JSTOR 2653207. MR 1723002.
• Mathis, Frank H.; Mathis, Lenora Jane (1995). "A nonlinear programming algorithm for hospital management". SIAM Review. 37 (2): 230–234. doi:10.1137/1037046. JSTOR 2132826. MR 1343214.
• Murty, Katta G. (1983). "3.10 Fractional programming (pp. 160–164)". Linear programming. New York: John Wiley & Sons, Inc. pp. xix+482. ISBN 978-0-471-09725-9. MR 0720547.CS1 maint: ref=harv (link)

• Bajalinov, E. B. (2003). Linear-Fractional Programming: Theory, Methods, Applications and Software. Boston: Kluwer Academic Publishers.
• Barros, Ana Isabel (1998). Discrete and fractional programming techniques for location models. Combinatorial Optimization. 3. Dordrecht: Kluwer Academic Publishers. pp. xviii+178. ISBN 978-0-7923-5002-6. MR 1626973.
• Martos, Béla (1975). Nonlinear programming: Theory and methods. Amsterdam-Oxford: North-Holland Publishing Co. p. 279. ISBN 978-0-7204-2817-9. MR 0496692.
• Schaible, S. (1995). "Fractional programming". In Reiner Horst and Panos M. Pardalos (ed.). Handbook of global optimization. Nonconvex optimization and its applications. 2. Dordrecht: Kluwer Academic Publishers. pp. 495–608. ISBN 978-0-7923-3120-9. MR 1377091.
• Stancu-Minasian, I. M. (1997). Fractional programming: Theory, methods and applications. Mathematics and its applications. 409. Translated by Victor Giurgiutiu from the 1992 Romanian. Dordrecht: Kluwer Academic Publishers Group. pp. viii+418. ISBN 978-0-7923-4580-0. MR 1472981.

• WinGULF – educational interactive linear and linear-fractional programming solver with a lot of special options (pivoting, pricing, branching variables, etc.)
• JOptimizer – Java convex optimization library (open source)