# The leading coefficient of Lascoux polynomials

@article{Borz2021TheLC, title={The leading coefficient of Lascoux polynomials}, author={Alessio Borz̀ı and Xiangying Chen and Harshit J. Motwani and Lorenzo Venturello and Martin Vodicka}, journal={Discret. Math.}, year={2021}, volume={346}, pages={113217} }

## One Citation

### A characterization of the algebraic degree in semidefinite programming

- Mathematics, Computer ScienceCollectanea Mathematica
- 2022

This characterization of the algebraic degree allows us to use the theory of symmetric polynomials to obtain many interesting results of Nie, Ranestad and Sturmfels in a simpler way.

## References

SHOWING 1-7 OF 7 REFERENCES

### Complete quadrics: Schubert calculus for Gaussian models and semidefinite programming

- Mathematics
- 2020

We establish connections between: the maximum likelihood degree (ML-degree) for linear concentration models, the algebraic degree of semidefinite programming (SDP), and Schubert calculus for complete…

### Maximum Likelihood Degree, Complete Quadrics, and ℂ*-Action

- MathematicsSIAM J. Appl. Algebra Geom.
- 2021

An explicit, basic, albeit of high computational complexity, formula is provided for the maximum likelihood degree of linear concentration models in algebraic statistics by relating an intersection problem on the variety of complete quadrics.

### The algebraic degree of semidefinite programming

- Mathematics, Computer ScienceMath. Program.
- 2010

Given a generic semidefinite program, specified by matrices with rational entries, each coordinate of its optimal solution is an algebraic number. We study the degree of the minimal polynomials of…

### A general formula for the algebraic degree in semidefinite programming

- Mathematics
- 2007

In this article, we use a natural desingularization of the conormal variety of (n × n)‐symmetric matrices of rank at most r to find a general formula for the algebraic degree in semidefinite…

### Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry

- Mathematics
- 2009

We study multivariate normal models that are described by linear constraints on the inverse of the covariance matrix. Maximum likelihood estimation for such models leads to the problem of maximizing…

### Estimation of covariance matrices which are linear combinations or whose inverses are linear combinations of given matrices. In Essays in Probability and Statistics, pages 1–24

- 1970