Practical Piecewise-Linear Approximation for Monotropic Optimization
INFORMS Journal on Computing
Deterministic Global Optimization: Theory, Methods and (NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 37) (Nonconvex Optimization and Its Applications)
Approximating separable nonlinear functions via mixed zero-one programs
Operations Research Letters
An algorithm for approximating piecewise linear concave functions from sample gradients
Operations Research Letters
A Note on “A Superior Representation Method for Piecewise Linear Functions”
INFORMS Journal on Computing
An Efficient Global Approach for Posynomial Geometric Programming Problems
INFORMS Journal on Computing
Base-2 Expansions for Linearizing Products of Functions of Discrete Variables
Operations Research
Base-2 Expansions for Linearizing Products of Functions of Discrete Variables
Operations Research
INFORMS Journal on Computing
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Many nonlinear programs can be piecewisely linearized by adding extra binary variables. For the last four decades, several techniques of formulating a piecewise linear function have been developed. By expressing a piecewise linear function with m + 1 break points, the current method requires us to use m additional binary variables and 4m constraints, which causes heavy computation when m is large. This study proposes a superior way of expressing the same piecewise linear function, where only ⌈ log 2m ⌉ binary variables and 8 + 8 ⌈ log 2m ⌉ additive constraints are used. Various numerical experiments demonstrate that the proposed method is more computationally efficient than current methods.