Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and
  Heuristics   In this paper we consider the problem of learning a regression function
without assuming its functional form. This problem is referred to as symbolic
regression. An expression tree is typically used to represent a solution
function, which is determined by assigning operators and operands to the nodes.
The symbolic regression problem can be formulated as a nonconvex mixed-integer
nonlinear program (MINLP), where binary variables are used to assign operators
and nonlinear expressions are used to propagate data values through nonlinear
operators such as square, square root, and exponential. We extend this
formulation by adding new cuts that improve the solution of this challenging
MINLP. We also propose a heuristic that iteratively builds an expression tree
by solving a restricted MINLP. We perform computational experiments and compare
our approach with a mixed-integer program-based method and a
neural-network-based method from the literature.

21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat