ReadME updates

Author: ft-adm

CVaROptBenchmarks.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "If you can solve all problems using a Python API significantly faster than in this Notebook, and your implementation is not publicly available, please contant us at software@fortitudo.tech. We might be willing to buy your implementation.\n",
     "\n",
-    "See the README file for more information on the CPU used to compute the results."
+    "The CVaR optimization problems below have been solved using the following CPU: 11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz (2.30 GHz)"
    ]
   },
   {

OptimizationExample.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "These problems are the easiest to solve, so they are a good starting point for understanding the data. After becoming comfortable with these problems, you can proceed to the more challenging ones that include derivatives, leverage constraints, transaction costs and risk budgets.\n",
     "\n",
-    "To see how the results have been computed using Fortitudo Technologies' Investment Analysis module, see the CVaROptBenchmarks.ipynb file."
+    "To see how the results have been computed using Fortitudo Technologies' Investment Analysis module, see the CVaROptBenchmarks.ipynb file: https://github.com/fortitudo-tech/cvar-optimization-benchmarks/blob/main/CVaROptBenchmarks.ipynb "
    ]
   },
   {

README.md

@@ -3,4 +3,6 @@ This repository contains Conditional Value-at-Risk (CVaR) portfolio optimization
 
 The starting point is the [next-generation investment framework's market represensation](https://youtu.be/4ESigySdGf8?si=yWYuP9te1K1RBU7j&t=46) given by the matrix $R\in \mathbb{R}^{S\times I}$ and associated joint scenario probability vector $p\in \mathbb{R}^{S}$.
 
+The [CVaROptBenchmarks notebook](https://github.com/fortitudo-tech/cvar-optimization-benchmarks/blob/main/CVaROptBenchmarks.ipynb) illustrates how the benchmark problems can be solved using Fortitudo Technologies' Investment Analysis module, while the [OptimizationExample notebook](https://github.com/fortitudo-tech/cvar-optimization-benchmarks/blob/main/OptimizationExample.ipynb) shows how you can replicate the results using the [fortitudo.tech open-source Python package](https://github.com/fortitudo-tech/fortitudo.tech) for the efficient frontier optimizations, which are the easiest problems to solve.
+
 You can read much more about the next-generation investment framework in the [Portfolio Construction and Risk Management book](https://antonvorobets.substack.com/p/pcrm-book).