May 1, 2024
Updated May 11, 2025
23 minute read
At a high level, a solver is a specialized piece of mathematical software designed to find solutions to mathematical problems. These problems are typically expressed in a generic form, allowing the solver to be applied to a wide range of similar challenges. Think of it as a highly intelligent calculator that, instead of just performing arithmetic, can tackle complex scenarios with many variables and constraints, aiming to find the best possible outcome. This could mean minimizing costs, maximizing efficiency, or finding the optimal allocation of resources.
Working with solvers can be intellectually stimulating. It involves translating real-world complexities into structured mathematical models, a process that requires both analytical rigor and creative problem-solving. The thrill of seeing a solver successfully untangle an intricate problem and provide a clear, actionable solution is a significant draw for many in this field. Furthermore, the broad applicability of solvers means that professionals can contribute to diverse industries, from shaping financial strategies to optimizing global supply chains or even advancing scientific research. This variety keeps the work engaging and ensures a continuous learning curve.
Introduction to Solvers
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Find a path to becoming a Solver. Learn more at:
OpenCourser.com/topic/uc9vmz/solve
Reading list
We've selected 23 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Solver.
Is an in-depth guide to using Solver, covering topics such as setting up optimization models, solving linear and nonlinear problems, performing sensitivity analysis, and more.
Is an excellent starting point for gaining a broad understanding of optimization and simulation within a business context. It emphasizes model building using spreadsheets, making it highly relevant to the courses mentioned. It's widely used as an MBA textbook and is valuable for both students and professionals seeking practical application.
Directly relevant to courses focusing on Excel Solver, this book details how to use Excel's tools for data analysis and business modeling, including optimization. It provides practical examples and guides users step-by-step. This is particularly useful for those who need to apply 'Solver' concepts within a familiar spreadsheet environment.
Considered a seminal work in the field, this book dives deep into convex optimization theory and applications. While more mathematically rigorous, it's a must-read for those seeking a deep understanding of a powerful class of optimization problems. It's highly recommended for graduate students and researchers.
Excels at teaching the art of translating real-world problems into mathematical optimization models. It's a valuable resource for anyone who needs to formulate problems before using a 'Solver'. It focuses on the modeling aspect rather than just the algorithms.
A strong theoretical foundation in linear optimization is crucial for understanding many 'Solver' applications. provides a rigorous introduction to the subject and is suitable for advanced undergraduate or graduate students. It prepares readers for more advanced topics like convex optimization.
Practical guide to using Solver, with a focus on real-world optimization problems.
Offers a very practical, hands-on approach to using Excel Solver. It provides detailed instructions and screenshots for solving various optimization problems. It's an excellent supplementary resource for those learning to implement optimization models in Excel.
Provides a comprehensive understanding of analytical tools for modeling, analyzing, and designing business processes, including simulation-optimization. It bridges the gap between process management and simulation, offering valuable insights for applying 'Solver' in business process improvement.
A widely used textbook for introductory operations research courses. It covers linear programming, integer programming, network models, and other relevant topics with a good balance of theory and applications. It's a solid resource for building a foundational understanding for using 'Solver'.
Covers both the theory and numerical methods for optimization. It valuable reference for understanding the algorithms that 'Solver' tools employ to find solutions. Suitable for graduate students and researchers interested in the computational aspects.
A classic text focused specifically on linear programming, a core component of many 'Solver' applications. It provides a solid understanding of the methods and a wide range of applications. Useful for building a deeper theoretical understanding of linear optimization.
This textbook offers an accessible treatment of optimization theory and methods, with an emphasis on engineering design. It provides fundamental background in linear algebra, geometry, and calculus, making it suitable for undergraduate students. It helps solidify the theoretical understanding behind 'Solver' tools.
Is an introductory textbook on operations research, which includes a chapter on optimization and discusses Solver.
A book aimed at a non-technical business audience, explaining how optimization (also known as business analytics or operations research) can be used to improve decision-making and drive profitability. It provides real-life examples and case studies, making it relevant for understanding the strategic importance of 'Solver' applications.
This text offers a practical guide to building and implementing simulation models in business situations. It covers various steps in a simulation study and discusses the use of simulation software, which is relevant to the simulation aspect of 'Solver' and related tools.
For those delving deeper into specific optimization types, this book provides a comprehensive treatment of integer programming. It's a more advanced text suitable for graduate students and researchers, offering in-depth knowledge relevant to solving problems with discrete variables using 'Solver'.
Comprehensive guide to using MATLAB for linear and integer optimization, which can be used with Solver.
Introduces system dynamics modeling, a complementary approach to discrete-event simulation often used in business analytics. It helps in understanding complex systems and how feedback loops influence behavior, providing a broader perspective for modeling with 'Solver' tools.
Presents a different perspective on teaching optimization, focusing on decision-making in dynamic and uncertain environments. While not a traditional textbook, it offers valuable insights into the evolving landscape of optimization and can broaden the understanding of how 'Solver' fits into a larger decision-making framework. Published in 2024, it covers contemporary thinking.
While focused on data mining, this book is relevant as data analysis often precedes or complements optimization and simulation in business analytics. It uses XLMiner, a tool that integrates with Excel, making it practical for users familiar with the spreadsheet environment and relevant to the broader context of 'Solver' applications.
This introductory book is designed for complete beginners who have no prior experience with Solver.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/uc9vmz/solve
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