Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simplifying deflation for non-convex optimization with applications in
  Bayesian inference and topology optimization   Non-convex optimization problems have multiple local optimal solutions.
Non-convex optimization problems are commonly found in numerous applications.
One of the methods recently proposed to efficiently explore multiple local
optimal solutions without random re-initialization relies on the concept of
deflation. In this paper, different ways to use deflation in non-convex
optimization and nonlinear system solving are discussed. A simple, general and
novel deflation constraint is proposed to enable the use of deflation together
with existing nonlinear programming solvers or nonlinear system solvers. The
connection between the proposed deflation constraint and a minimum distance
constraint is presented. Additionally, a number of variations of deflation
constraints and their limitations are discussed. Finally, a number of
applications of the proposed methodology in the fields of approximate Bayesian
inference and topology optimization are presented.

9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int