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The Optimization Toolbox extends the
MATLAB® technical computing environment
with tools and widely used algorithms for
standard and large-scale optimization. These
algorithms solve constrained and uncon-
strained continuous and discrete problems.
The toolbox includes functions for linear
programming, quadratic programming, non-
linear optimization, nonlinear least squares,
nonlinear equations, multi-objective optimi-
zation, and binary integer programming.
MATLAB and the Optimization Toolbox
let you easily define models, gather data,
manage model formulations, and analyze
results. They give engineers and scientists
the tools needed to find optimal solutions,
perform tradeoff analysis, balance multiple
design alternatives, and quickly incorporate
optimization methods in their algorithms
and models.
Toolbox functions, which can be accessed via
the MATLAB command line, are written in the
open MATLAB language. This means that you
can inspect the algorithms, modify the source
code, and create your own custom functions.
Optimization Toolbox 3
Solve standard and large-scale optimization problems
KEY FEATURES
■ Tools for defining, solving, and assessing optimization
problems
■ Solvers for nonlinear optimization and multi-objective
optimization
■ Solvers for nonlinear least-squares, data fitting, and nonlinear
equations
■ Methods for solving quadratic and linear programming
problems
■ Methods for solving binary integer programming problems
A blurred image is recovered using the
large-scale linear least squares algorithm
in the Optimization Toolbox.