1) Linear optimization: define a linear optimization problem min ax+by +cz+d i) subject to upper and lower bounds, to ensure that the domain is a finite ii) an equality constraint that will allow you to express actually z as a function of x and y, this means your problem is actually 2 dimensional so that you can the domain it in 3-space iii) an inequality constraint to restrict the domain You first solve the problem by hand. We know that the extremum is on the boundary. Evaluate your cost function on the boundary and find out the point at which it is lowest. 2) Solve the same problem with MATLAB using linprog function 3) Solve a quadratic programming problem based on asset allocation: Use the H matrix as H=[0.4 0.2 0.1 -0.05 0.2 0.4 -0.1 0.15 0.1 -0.1 0.4 0.15 -0.05 0.15 0.15 0.4] Choose the inequality and equality constraints as you wish. Give an example where there is a feasible solution and antoher example with no feasible solution