How can you ensure that your algorithms meet the goals of your clients?

Understanding algorithmic problems involves grasping the problem's underlying logic and devising efficient step-by-step solutions. For example, consider the problem of finding the maximum value in an array of numbers. To understand this algorithmic problem, you would recognize the need to iterate through the array, keeping track of the maximum value encountered so far. By identifying the problem's core operations and constraints, you can develop an algorithm that efficiently solves it, which is a fundamental skill in computer science and programming.

Understand the business first. Before developing an algorithm that solves a problem for a client, you have to understand the problem, but first you have to understand the business. A very common problem when developing software solutions is believing that the client is interested in information systems because of their technical functionalities. However, for the client a problem is important to the same extent that its solution helps achieve the company's objectives. Understanding the business allows us to see the problems to be solved from the client's perspective and, in this way, it is possible to prioritize improvements to our solution. The objective of any technological solution, ultimately, is to add value to the business.

Ensuring that your algorithms meet the goals of your clients involves a structured approach from understanding the client’s requirements to the deployment and monitoring of the algorithm. In addition, you should involve continuous engagement, iterative development, and a thorough understanding of both the problem domain and the client’s specific objectives. Always be adaptable and receptive to feedback, and prioritize clear communication and transparency throughout the development and deployment process.

Our group usually starts by discussing the issues the client is facing: What does it involve? What has been done about it? What are the obstacles? Then we start working on the modeling of the problem, i.e., casting the verbal description of the problem into a mathematical framework. The model is shared with the client together with some use cases and (if needed) some education. To make sure that the model is a good match and to implement it properly, we start to work with the client to find the manpower, consisting of students, research assocaites and postdoctoral fellows to work on the implementation and improvement of the algoriths.

The first step, as always, is to fully grasp the problem at hand. What are the inputs? What are the desired outputs? What constraints must the algorithm adhere to? And perhaps most importantly, what objectives are we aiming to achieve? Equally important is communication. Before diving into code, I make it a point to sit down with the clients and stakeholders, asking probing questions to clarify any ambiguities or uncertainties. It's through these conversations that we iron out the finer details, ensuring that everyone is on the same page before we proceed.

There is almost always multiple available approaches to solve a problem. It's usually a tradeoff between time complexity and implementation effort. A faster approach usually requires more complicated implementation and requires more engineering effort and more error-prone to make it bug free. It's not good to choose a brute force algorithm that cannot scale with data size, but less obviously it's as bad to overly optimize the algorithm that is not in the performance critical path of the system.

Choosing the right approach to solving an algorithmic problem is pivotal for efficiency and effectiveness. It involves recognizing the problem's nature and constraints and selecting the most suitable algorithmic technique. For instance, when tackling a sorting problem with a small dataset, a straightforward bubble sort might suffice, but for larger datasets, a more efficient algorithm like merge sort or quicksort would be a better choice. Understanding the problem's size, data characteristics, and desired outcomes guides the selection of the most appropriate algorithmic approach, ensuring optimal solutions are achieved.

In problem-solving, there is always more than one way to tackle it. Take your time, consider various options, and iterate as needed. I start with a high-level algorithm that addresses the core problem. Save the tiny details for later iterations. Ex: if you are creating an algorithm to estimate the sentiment of text, begin with key steps like preprocessing, initializing sentiment scoring, analyzing the text, aggregating scores, and determining sentiment labels. Then gradually add and adjust details to solve. For example, text cleaning, removing stop words, and normalization for the preprocessing step. Repeat this process multiple times until no improvements are seen. This approach has helped me in developing intricate algorithms.

First understand the problem in the simplest way, that means how to get a trivial solution. Post that you try to consider optimizing factors. Pen down the solution or draw a flowchart. Break into smaller modules & find ways to optimize them wherever possible. Sometimes complete other approaches than the initial trivial solution do better. The best way to find them is to take some sample data examples & solve the problem manually first before moving further.

Start with the end goal, end-state, of how the complete deployed solution looks like. Then imagine how it feels to the user or the client. This helps in prioritizing the right problems to solve and in picking the right algorithmic trade-offs. The approach must be determined by the end-goal.

Implementing and testing your algorithm is the practical step in turning your algorithmic solution into a functional program. Once you've chosen the right approach and translated it into code, rigorous testing is essential. For instance, if you've designed an algorithm to find the shortest path in a graph using Dijkstra's algorithm, you'd need to write the code and then test it with various graph structures to ensure it produces accurate and efficient results. Comprehensive testing helps uncover potential errors and ensures your algorithm functions correctly across different scenarios, making it a crucial phase in the software development process.

Testing is inevitable. Test not only the new change but also the peripheral areas is the key. It is important to be reliable.

Testing is key here! You don't want to claim you've finished something and it's rigorously tested by someone else to show extensive faults. Test for different edge-cases and focus on optimizing! Simulating real-world conditions and having several test data sets is a good idea.

Implementing and testing your algorithm is the phase where your theoretical solution transforms into a practical and functional tool. This stage is crucial for ensuring that your algorithm not only works but also performs well under various conditions. The implementation and testing phase is where theory meets reality, and your algorithm starts to take shape as a functional solution. Attention to coding practices, rigorous testing, and a commitment to quality are crucial to building a reliable and effective algorithm that meets the objectives and expectations set in the earlier stages of problem-solving.

Two key points must be considered when implementing the solution: 1 - Solution architecture 2- Validation and testing according to the acceptance criteria; The architecture must take into account various non-functional requirements such as security, scalability, maintainability, availability, the software and hardware platform to be used and various issues that not only ensure that the proposal meets the functional requirements, but that it does so efficiently, scalably, securely and with guaranteed maintenance. On the second point, it is important to carry out several test cases, preferably automated ones that simulate as closely as possible the conditions in which the algorithm will be executed in the final environment.

Build a proof-of-concept first. It could be partially tested version of the solution. Do a demo/dry-run and understand what needs fine-tuning and bug-fixing. Involve your users in Alpha and Beta testing

Ensuring that your algorithm aligns with client requirements is a crucial step in software development. This involves thorough validation and verification against the specified criteria. For example, if your client requires a scheduling algorithm for a transportation system, you must confirm that it minimizes delays and optimizes resource utilization, meeting the client's performance and efficiency expectations. Regular communication with the client and rigorous testing against their specific needs and use cases are essential to guarantee that your algorithm fulfills their requirements and contributes to a successful project outcome.

Most of the time we only demonstrate business solution to our clients. They can do some regression tests but there is very limited check spots can clients do about reliability of algorithms. First they should be sure about development team's qualification, then can request some reports about sonar analysis, performance metrics etc.

The main point before the solution goes into production is to perform a proof of concept (PoC) in a controlled environment very close to the real one for a period of time and collect as much information as possible about the execution of the solution in this PoC. Based on these collected data, perform: bug fixes, usability adjustments, validation of acceptance criteria in the real environment, performance adjustments if necessary, and critical analysis of the solution. Finally, it is of great importance to prepare a presentation focusing on the key points of the solution and the results achieved for the sponsors and decision-makers in the company's business area.

Communicating and demonstrating your algorithm is the final and crucial step in the development process. It's where you bridge the gap between your technical work and your clients or stakeholders' understanding and needs.By effectively communicating and demonstrating your algorithm, you not only validate the quality and functionality of your solution but also foster trust, credibility, and satisfaction among your clients. It's an opportunity to showcase your commitment to meeting their needs and delivering a solution that genuinely solves their problems.

When writing an algorithm for a use case, consider these key measures: - Clarity and Readability: Ensure clear and well-structured code. - Scalability: Design for efficiency as data sizes grow. - Robustness: Handle errors gracefully. - Modularity: Break down complex algorithms into reusable parts. - Optimization: Continuously improve performance. Security: Implement appropriate security measures. Algorithmic Paradigms: Choose the right algorithmic approach. - Concurrency: Leverage parallel processing when applicable. - Resource Efficiency: Optimize memory and CPU usage. - Feedback: Seek input and iterate for improvements. Legal and Ethical: Comply with legal and ethical guidelines.

The algorithms should also be well tested to handle all aspects of usage. Additionally there should be a robust automated test suite that ensures the program is working as expected even after shipping. Any new change should go through the test suites that run in different environments before reaching production. There should also be monitoring to ensure the program is performing as efficiently as expected. These are just few of the additional aspects but there are even more aspects to ensure the code is working as expected and meets all the requirements of clients. Especially after the initial release.

Maintainability, Readability and flow of the algorithm. Why are above things important? Because you are not the only person working on the project and you are not always available for maintenance of that piece of code.

A final point to note is whether the solution complies with specific or general legal regulations such as privacy, data security and adjusting with the legal department if contracts and agreements are required.