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# The class of submodular functions is [[closure (mathematics)|closed]] under non-negative [[linear combination]]s. Consider any submodular function <math>f_1,f_2,\ldots,f_k</math> and non-negative numbers <math>\alpha_1,\alpha_2,\ldots,\alpha_k</math>. Then the function <math>g</math> defined by <math>g(S)=\sum_{i=1}^k \alpha_i f_i(S)</math> is submodular. Furthermore, for any submodular function <math>f</math>, the function defined by <math>g(S)=f(\Omega \setminus S)</math> is submodular. The function <math>g(S)=\min(f(S),c)</math>, where <math>c</math> is a real number, is submodular whenever <math>f</math> is monotonic.
# If <math>f:2^\Omega\rightarrow \mathbb{R}_+</math> is a submodular function then <math>g:2^\Omega\rightarrow \mathbb{R}_+</math> defined as <math>g(S)=\phi(f(S))</math> where <math>\phi</math> is a [[concave]] function, is also a submodular function.
# Consider a random process where a set <math>T</math> is chosen with each element in <math>\Omega</math> being included in <math>T</math> independently with probability <math>p</math>. Then the following inequality is true <math>\mathbb{E}[f(T)]\geq p f(\Omega)+(1-p) f(\
== Optimization problems ==
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