Minimizing combinatoric sum of piecewise-linear functions. A convex piecewise-linear function can be defined as f_i(x)=max_{j=1,⋯,m} a_j x+b_j where a_j, b_j, x in R.

nar6jetaime86

nar6jetaime86

Answered question

2022-09-04

Minimizing combinatoric sum of piecewise-linear functions
A convex piecewise-linear function can be defined as f i ( x ) = max j = 1 , , m a j x + b j where a j , b j , x R .
To find its minimum, I can construct a linear program using auxiliary scalar variable t. Namely:
minimize  t  subject to  a i x + b i t , i = 1 , m
And i can use similar strategy when solving minum of f 1 ( x ) + f 2 ( x ).
Suppose i have a set of piecewise-linear functions F = { f 1 ( x ) , , f i ( x ) , , f n ( x ) }.
How can i efficiently find an optimal partition (L,R) of F such that the min l L f l ( x ) + min k R f k ( x ) is minimized ?

Answer & Explanation

Sanaa Holder

Sanaa Holder

Beginner2022-09-05Added 20 answers

Step 1
Introduce a binary decision variable z i to indicate whether i L. Now minimize t 1 + t 2 subject to linear big-M constraints
(1) a i x + b i t 1 M i ( 1 z i ) for  i { 1 , , m } (2) a i x + b i t 2 M i z i for  i { 1 , , m }
Step 2
Constraint (1) enforces the logical implication i L a i x + b i t 1 . Constraint (2) enforces the logical implication i R a i x + b i t 2 .

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