CVXR | An R modeling language for convex optimization problems | Math library

I would like to write the log likelihood of the Dirichlet density as a disciplined convex programming (DCP) optimization problem with respect to the parameters of the Dirichlet distribution alpha. However, the log likelihood

I use MOSEK to run a very large linear programming problem in Matlab (32768 unknowns and 691621 constraints). The code is submitted in a Linux based cluster. In the bash file I request the following amount of memory:

I am trying to write a wrapper function around CVXR, such that the 'objective' and 'constraint' can be passed by a function. I using the following example:

Example:

I have encountered several optimization problems that involve identifying one or more indices in a vector that maximizes or minimizes a cost. Is there a way to identify such indices in linear programming? I'm open to solutions in mathprog, CVXR, CVXPY, or any other API.

For example, identifying an index is needed for change point problems (find the index at which the function changes), putting distance constraints on the traveling salesman problem (visit city X before cumulative distance Y).

As a simple example, suppose we want to identify the location in a vector where the sum on either side is the most equal (their difference is smallest). In this example, the solution is index 5:

I'm trying to minimize this function:

\min_{\mu} \sum_{t=T}^T \| y_t - \mu_t \|2 + \lambda \sum{t=1}^{T-1} \|mu_{t+1}-\mu_{t}\|_2

where: y and mu are p*T matraxis. Everything compiles well until I use the solve() function.

Here is what I coded with y being a p*obs matrix

Hello I am trying to run the example from here: http://rtutorial.altervista.org/lp_solvers.html

A snippet and test where it goes wrong:

I'm doing empirical likelihood maximization, and hit an error. The statistics is a bit complex and I describe the error like following.

Suppose I have multivariate function with domain: ,

And I want to minimize the function in the domain.

Here is the counter plot for my function, the optimum seems to be x = y = 0.5 and z = 0.

I am trying to map two lists over myEq with a third numeric input which is not the same length as mus and Sigmas.

The equation I want to map over:

I am trying to apply map2 over a series of lists I have to solve a convex optimisation problem.

With the data I try the following function to solve my equation.