Yes, the Inbuilt optimization toolboxes of MATLAB help to solve large-scale LP problems. The optimization toolbox in MATLAB provides functions and algorithms for solving a variety of optimization problems, including LP. For very large-scale LP problems with millions of variables or constraints, specialized solvers or parallel computing techniques may be necessary to achieve efficient solutions. In such cases, MATLAB provides options to interface with external solvers or parallel computing frameworks to handle the computational requirements.

It is possible to solve large-scale linear programming (LP) problems using MATLAB. The MATLAB optimization toolbox includes functions and solvers specifically designed for the solution of LP problems and other types of optimization problems.

There are several LP solvers available in the MATLAB Optimization Toolbox, including the built-in linprog function, which is capable of resolving both small and large-scale LP problems. The Linprog program supports both sparse and dense matrix representations, which allows it to handle large constraint matrices efficiently.

It is also possible to use external solvers through MATLAB's "Optimization Toolbox Interface for Linear Programming" (OTI-LP). Through OTI-LP, you are able to interact with third-party solvers that may be better suited to handling large-scale problems.

The computational resources required to solve large-scale LP problems, such as memory and processing power, should be considered when analyzing LP problems on a large scale. As part of MATLAB, users can manage memory usage and optimize performance by using sparse matrix representations, parallel computing, and memory-efficient algorithms. For example, users can use the MATLAB ‘parfor’ command to evaluate a function with different inputs in parallel, allowing for faster processing on multicore and cluster computers.