cvxpy | embedded modeling language for convex optimization problems

I try to solve the convex problem on page 20 presented in this paper

In my opinion, the objective is convex.

cvxpy version 1.1.18

prob is DCP: False

it reports

xception has occurred: DCPError Problem does not follow DCP rules. Specifically: The objective is not DCP. Its following subexpressions are not: 0.004 / (power(var1[0], 0.5) + power(var1[0], 0.5)) 0.004 / (power(var11, 0.5) + power(var11, 0.5))

my code is

We are using Mosek solver via its Cvxpy interface.

We deal with large-scale optimization problem on a regular-basis and sometimes the runtime is very high. So, we specify a upper limit on runtime using Mosek's mosek.dparam.optimizer_max_time parameter.

In those cases, the pain-point is that we get no solution.

Is it possible to get the suboptimal/best found solution so far?

I am solving the currency exchange problem, where we have a set of currency holdings for 10 different kinds. Our objective is to have a specific minimum of each currency holdings, while minimizing the loss due to the bid-ask spread which occurs when we exchange some units of a currency for some units of another currency.

In case it helps here is the full question: https://drive.google.com/file/d/1DG8xfM3ayQ0Oj9VQGYSNbHF_rbKHMTLq/view

The code:

Previously I used command conda install -c mosek mosek to install mosek(my IDE is VS Code and use anaconda environment). After I installed it, I ran a program for a convex optimization problem, and one line of code was(because I want to choose mosek as solver):

I am trying to use maxrect's get_maximal_rectangle, which uses cvxpy under the hood.

With the shapely polygon: POLYGON ((0 0.95, 0 2, 2 2, 2 1.95, 0 0.95))

First of all, I'm sorry if my questions doesn't make sens, I am new using CVXPY library and I don't understand everything :/

I am trying to solve a minimization problem that I thought would be easy handle. I got a matrix S dimensions (9,7) with known coefficients, B dimensions (1,7) with known coefficients, Alpha dimensions (1,7) what I need to find, with various constraints :

• Alpha must be positive
• The sum of all the coefficients of Alpha must be equal to 1

I need to optimize Alpha such as : A @ Alpha-B=0. I discovered CVXPY and thought least square optimization was perfect for this issue.

This is the code I wrote :

I want to solve linear regression in the following way

When I try with minimizing the sum of cost it works fine,

I'm trying to solve the following problem in CVXPY.

The problem is a mixed-integer SDP due to the PSD matrix we're solving. However, according to this list it looks as though none of the solvers can handle such a problem.

Can we use the fact that A is a 2x2 matrix to somehow convert this to a mixed-integer SOCP problem?

data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data

I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.