  Finding a \emph{single} best solution is the most common objective in
combinatorial optimization problems. However, such a single solution may not be
applicable to real-world problems as objective functions and constraints are
only "approximately" formulated for original real-world problems. To solve this
issue, finding \emph{multiple} solutions is a natural direction, and diversity
of solutions is an important concept in this context. Unfortunately, finding
diverse solutions is much harder than finding a single solution. To cope with
difficulty, we investigate the approximability of finding diverse solutions. As
a main result, we propose a framework to design approximation algorithms for
finding diverse solutions, which yields several outcomes including
constant-factor approximation algorithms for finding diverse matchings in
graphs and diverse common bases in two matroids and PTASes for finding diverse
minimum cuts and interval schedulings.
