R Tutorial : Challenges of portfolio optimization

Want to learn more? Take the full course at https://learn.datacamp.com/courses/intermediate-portfolio-analysis-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Portfolio optimization is a hard problem. One challenge is knowing what solver to use and the capabilities and limits of the chosen solver. A solver is an algorithm designed to find the optimal solution to a given problem. You can chose a solver based on the objective or formulate the objective to fit the chosen solver. Ideally, you want the flexibility of effortlessly switching between solvers depending on the problem. In many cases, there is more than one solver that can solve the problem and you should evaluate both. An example of a closed-form solver is a quadratic programming solver. The main advantage of closed-form solvers is that they solve a given problem very fast and efficiently and return an exact solution. The main drawback is that the problem must be formulated in a very specific way that is typically unique to the solver. An example of a global solver is differential evolution optimization. Global solvers have the advantage of being able to solve more general types of problems and find the approximate solution of the global minimum or maximum of the objective function with local minima or maxima. However, the algorithms used in global solvers are relatively more complex and more compute-intensive. In this course, you will use both closed-form and global solvers and learn how PortfolioAnalytics overcomes these challenges of portfolio optimization. For this next example, you will consider a portfolio optimization problem where the objective is to maximize quadratic utility, subject to the constraints such that the weight of each asset must be greater than or equal to zero and the weights must sum to 1. The intuition behind this formulation is that you maximize portfolio return, with a penalty term for portfolio variance. The