# 1 Introduction¶

The **MOSEK** Optimization Suite 10.1.21 is a powerful software package capable of solving large-scale optimization problems of the following kind:

linear,

conic:

conic quadratic (also known as second-order cone),

involving the exponential cone,

involving the power cone,

semidefinite,

convex quadratic and quadratically constrained,

integer.

In order to obtain an overview of features in the **MOSEK** Optimization Suite consult the product introduction guide.

The most widespread class of optimization problems is *linear optimization problems*, where all relations are linear. The tremendous success of both applications and theory of linear optimization can be ascribed to the following factors:

The required data are simple, i.e. just matrices and vectors.

Convexity is guaranteed since the problem is convex by construction.

Linear functions are trivially differentiable.

There exist very efficient algorithms and software for solving linear problems.

Duality properties for linear optimization are nice and simple.

Even if the linear optimization model is only an approximation to the true problem at hand, the advantages of linear optimization may outweigh the disadvantages. In some cases, however, the problem formulation is inherently nonlinear and a linear approximation is either intractable or inadequate. *Conic optimization* has proved to be a very expressive and powerful way to introduce nonlinearities, while preserving all the nice properties of linear optimization listed above.

The fundamental expression in linear optimization is a linear expression of the form

In conic optimization this is replaced with a wider class of constraints

where \(\K\) is a *convex cone*. For example in 3 dimensions \(\K\) may correspond to an ice cream cone. The conic optimizer in **MOSEK** supports a number of different types of cones \(\K\), which allows a surprisingly large number of nonlinear relations to be modeled, as described in the **MOSEK** Modeling Cookbook, while preserving the nice algorithmic and theoretical properties of linear optimization.

## 1.1 Why the Optimizer API for Python?¶

The Optimizer API for Python provides an object-oriented interface to the **MOSEK** optimizers. This object oriented design is common to Java, Python and .NET and is based on a thin class-based interface to the native C optimizer API. The overhead introduced by this mapping is minimal.

The Optimizer API for Python can be used with any application running on recent Python 3 interpreters. It consists of a single `mosek`

package which can be used in Python scripts and interactive shells making it suited for fast prototyping and inspection of models.

The Optimizer API for Python provides access to:

Linear Optimization (LO)

Conic Quadratic (Second-Order Cone) Optimization (CQO, SOCO)

Power Cone Optimization

Conic Exponential Optimization (CEO)

Convex Quadratic and Quadratically Constrained Optimization (QO, QCQO)

Semidefinite Optimization (SDO)

Mixed-Integer Optimization (MIO)

as well as to additional functions for

problem analysis,

sensitivity analysis,

infeasibility diagnostics,

BLAS/LAPACK linear algebra routines.