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One of the most important concepts that you should master is algorithmic techniques. While most students consider algorithm design as programming, it is just a set of instructions that are fed to a computer to get the desired result. This is the best platform to get help with algorithm homework if you are struggling with the task allotted to you. According to our experts, here are some of the points that you should keep in mind when designing an algorithm:
- Make sure that all your steps are clear
- An algorithm is the most effective way of giving instructions
- The input and output should be precisely defined
- An algorithm should be used in different programming languages. As a result, it shouldn’t contain any code
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Backtracking algorithm
This is a technique that is used to solve problems recursively by building the solution incrementally. Meaning, one at a time, the solutions that do not satisfy the set constraints of the problem at a given time are removed A good example of a backtracking algorithmic technique is the Sudoku solving problem. In this game, the player tries to fill the missing digits one by one. If a current digit cannot lead s to a solution we backtrack (remove it) and try another digit. This method is considered better than the naïve approach of coming up with all possible combination digits and then trying them out one by one.
Divide and conquer algorithm
The divide and conquer technique has three parts, divide, conquer and combine. First, we divide the problem into smaller chunks or sub-problems. Next, we recursively solve the sub-problems. Lastly, we combine the solutions of the sub-problems to get the final solution of the whole problem. Some standard algorithms follow the divide and conquer algorithm technique. They include, quicksort, merge sort, closest pair of points, Karatsuba algorithm for fast multiplication, Cooley-Tukey Fast Fourier transform algorithm, and Strassen's algorithm.
Randomized algorithm
This is an algorithmic technique that uses random numbers to decide on the next step to take. The random logic reduces time and space complexity in other random standard algorithms. For example, in a randomized quicksort, a random number is used to pick the next pivot. It is one of the most widely used algorithm design techniques because it makes logical and accurate decisions. Algorithms with deterministic time complexity are known as Monte-Carlo algorithms. These types of algorithms are often analyzed for the worst case. On the other hand, Las Vegas algorithms have time complexity that is dependent on the value of a random variable.
Greedy algorithm
This type of algorithmic paradigm builds up the solution piece by piece. The next piece it chooses is the one with an immediate or obvious benefit. It is fit for problems that require us to choose a locally optimal strategy that leads to a global solution. For example, in a Fractional Knapsack problem, our local optimal strategy is to choose the parameter with the maximum value versus weight ratio. Since we allow the fractions of the parameter to be taken, the strategy will lead to a global optimal solution.
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