The application of linear programming in financial decision-making is explored in the next section.

Personal project Q5. This question is concerned with the design and analysis of recursive algorithms. You are given a problem statement as shown below. This problem is concerned with performing calculations on a sequence ? of real numbers. Whilst this could be done using a conventional loop-based approach, your answer must be developed using a recursive algorithm. No marks will be given if your answer uses loops. FindAverageAndProduct(a1, ...., an) such that n > 1 Input: A sequence of real values A = (a1, ..., an) Output:, A 2-tuple (average, product) containing the average (average) of all the values and the product (product) of all the values of the elements in A. Your recursive algorithm should use a single recursive structure to find the average and product values, and should not use two separate instances of a recursive design. You should not employ any global variables. (a) Produce a pseudo code design for a recursive algorithm to solve this problem.   (b) Draw a call-stack…

Assume a very good NBA team has a 70% chance of winning in each game it plays. Use simulation to answer these questions, where each iteration of the simulation generates the outcomes of all 82 games. Use simulation with 10,000 iterations to answer the questions. For part a, write your numerical answer with no decimal or commas (e.g., if your computed answer is 101,021.128, write your answer as 101021). For part b, write your probability answer to two decimal places with a leading 0 (e.g., write a probability of 90.02% as 0.90). a. During an 82-game season what is the average length of the team's longest winning streak? The average length of the winning streak is  . b. What is the probability that the team has a winning streak of at least 16 games? The probability is

Conway's Game of Life: This is a zero person game with the following rules: (see Wikipedia for example) Any live cell with fewer than two live neighbours dies, as if by underpopulation. Any live cell with two or three live neighbours lives on to the next generation. Any live cell with more than three live neighbours dies, as if by overpopulation. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction. Remember the oscillator or blinker of 3 cells. You can also find this blinker on Wikipedia. 1 21 1 2 1 21 3. 4 6 4. 6. 4 8. 9 8 9 #1 #2. #3 5. Consider now these 3 creatures at stage 1: Show how they look like in the next two stages: stage 2 and stage 3. Explain how you get the answers Creature 1 Creature 2 Creature 3 (here creature 1 is the blinker of 3 cells, horizontally; creature 2 consists of two adjacent cells, creature 3 consists of 4 adjacent cells horiztonally) ww (d) Creature 1 (10%), (e) Creature 2 (8%), (f) Creature 3 (20%)

What is the significance of the fact that object function is more important than constraints in linear programming problems?

Can we use dynamic programming to generate a chain of decisions that depend on one another? I don't know what to do at this point.

Can we use dynamic programming to generate a chain of decisions that depend on one another? I don't know what to do at this point.

Carl is sitting in a chair that is next to a table. Carl’s cell phone is on top of the table. Your task is to direct Carl to pick up his cell phone. Does the solution to this problem require a repetition structure? If so, what needs to be repeated?

Can we use dynamic programming to generate a chain of decisions that depend on one another? I don't know what to do at this point.