From release 8.1.0.56 we initialize support for .NET Core, the cross-platform implementation of .NET.

MOSEK for .NET Core is distributed as a platform-independent NuGet package, which can be downloaded directly from our website. See the documentation for .NET APIs for installation instructions.

## Friday, June 29, 2018

## Friday, June 22, 2018

### MOSEK at ISMP 2018

Here is a complete schedule of our talks at ISMP 2018 in chronological order:

Check the program for any last-minute changes.

The session on Mixed-Integer Conic Optimization (Mon, 5:00PM) is organized by Sven Wiese. The full program of that session is:

Speaker | Title | Session | Time | Room |
---|---|---|---|---|

Sven Wiese | The Mixed-integer Conic Optimizer in MOSEK | Mixed-Integer Conic Optimization | Mon, 5:00PM | A. DURKHEIM, Bld. A |

Henrik Friberg | Projection and presolve in MOSEK: exponential and power cones | Theory and algorithms in conic linear programming 1 | Tue, 8:30AM | S. LC5, Bld. L |

Joachim Dahl | Extending MOSEK with exponential cones | Theory and algorithms in conic linear programming 2 | Wed, 8:30AM | S. 20, Bld. G |

Erling Andersen | MOSEK version 9 | Progress in Conic and MIP Solvers | Wed, 3:15PM | A. PITRES, Bld. O |

MichaĆ Adamaszek | Exponential cone in MOSEK: overview and applications | Relative Entropy Optimization I | Fri, 3:15PM | S. LC5, Bld. L |

Check the program for any last-minute changes.

The session on Mixed-Integer Conic Optimization (Mon, 5:00PM) is organized by Sven Wiese. The full program of that session is:

- Lucas Letocart, Exact methods based on SDP for the k-item quadratic knapsack problem
- Tristan Gally, Knapsack Constraints over the Positive Semidefinite Cone
- Sven Wiese, The Mixed-integer Conic Optimizer in MOSEK

## Tuesday, June 12, 2018

### Elementary intro to infeasibility certificates

We occasionally receive support questions asking about the meaning and practical use of infeasibility certificates, usually when the user expected the problem to be feasible. While an infeasibility certificate is a well-defined mathematical object based on Farkas' lemma, it is not intuitive for everyone how to use it to address the basic question of "What is wrong with my problem formulation?". We will try to explain this on a very simple example.

In this post we don't even consider optimization, but restrict to elementary linear algebra. Suppose we want to find a solution to a system of linear equations:

$$

\begin{array}{llllllllllll}

& 2x_1 & - & x_2 & + & x_3 & - & 3x_4 & + & x_5 & = & 1, \\

& & & x_2 & -& 2x_3 & & & +& x_5 & = & -1,\\

& 3x_1 & & & - & x_3 & - & x_4 & & & = & 2, \\

& 2x_1 & + & x_2 & - & 3x_3 & - & 3x_4 & + & 3x_5 & = & -0.5.

\end{array}

$$

This system of linear equations is in fact infeasible (has no solution). One way to see it is to multiply the equations by coefficients given on the right-hand side and add them up:

$$

\begin{array}{lllllllllllll}

& 2x_1 & - & x_2 & + & x_3 & - & 3x_4 & + & x_5 & = & 1 & / \cdot (-1) \\

& & & x_2 & -& 2x_3 & & & +& x_5 & = & -1 & / \cdot (-2) \\

& 3x_1 & & & - & x_3 & - & x_4 & & & = & 2 & / \cdot 0 \\

& 2x_1 & + & x_2 & - & 3x_3 & - & 3x_4 & + & 3x_5 & = & -0.5 & / \cdot 1 \\

\hline

& & & & & & & & & 0 & = & 0.5. &

\end{array}

$$

In this post we don't even consider optimization, but restrict to elementary linear algebra. Suppose we want to find a solution to a system of linear equations:

$$

\begin{array}{llllllllllll}

& 2x_1 & - & x_2 & + & x_3 & - & 3x_4 & + & x_5 & = & 1, \\

& & & x_2 & -& 2x_3 & & & +& x_5 & = & -1,\\

& 3x_1 & & & - & x_3 & - & x_4 & & & = & 2, \\

& 2x_1 & + & x_2 & - & 3x_3 & - & 3x_4 & + & 3x_5 & = & -0.5.

\end{array}

$$

This system of linear equations is in fact infeasible (has no solution). One way to see it is to multiply the equations by coefficients given on the right-hand side and add them up:

$$

\begin{array}{lllllllllllll}

& 2x_1 & - & x_2 & + & x_3 & - & 3x_4 & + & x_5 & = & 1 & / \cdot (-1) \\

& & & x_2 & -& 2x_3 & & & +& x_5 & = & -1 & / \cdot (-2) \\

& 3x_1 & & & - & x_3 & - & x_4 & & & = & 2 & / \cdot 0 \\

& 2x_1 & + & x_2 & - & 3x_3 & - & 3x_4 & + & 3x_5 & = & -0.5 & / \cdot 1 \\

\hline

& & & & & & & & & 0 & = & 0.5. &

\end{array}

$$

We get an obvious contradiction, which proves the system has no solution. The vector

$$ y = [-1, -2, 0, 1] $$

of weights used above is therefore a proof (certificate) of infeasibility. MOSEK produces such a certificate automatically. Here is a simple script in MATLAB that computes precisely that vector:

As output we get:

Here are a few comments:

###

In the example above $y_3=0$ which means that the third equation does not matter: infeasibility is caused already by some combination of the 1st, 2nd and 4th equation. In many practical situations the infeasibility certificate $y$ will have very few nonzeros, and those nonzeros determine a subproblem (subset of equations) which alone cause infeasibility. The user can configure MOSEK to print an infeasibility report, which in our example will look like:

As output we get:

Here are a few comments:

- General theory guarantees that the dual variable y is a convenient place that can store the certificate.
- When a system of equations has no solution then an appropriate certificate is guaranteed to exist. This is a basic variant of Farkas' lemma, but it should be intuitively clear: your favorite method of solving linear systems (for instance Gaussian elimination) boils down to taking successive linear combinations of equations, a process which ends up either with a solution or with an "obvious" contradiction of the form $0=1$.

### Using the certificate to debug a model

This report is nothing else than a listing of equations with nonzero entries in $y$, and $y$ is the difference between Dual lower and Dual upper. Analyzing this set (which hopefully is much smaller than the full problem) can help locate a possible modeling error which makes the problem infeasible.

To conclude, let us now phrase the above discussion in matrix notation. A linear equation system

$$ Ax=b $$

is infeasible if and only if there is a vector $y$ such that

$$ A^Ty = 0 \quad \mbox{and}\quad b^Ty \neq 0. $$

The situation is analogous for linear problems with inequality constraints. We will look at an example of that in another blog post.

Labels:
infeasibility

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