Linear Programming Tutors

Linear Programming (LP) is a mathematical technique for optimizing a linear objective function, subject to linear equality and inequality constraints. Widely used in Operations Research (OR), LP helps in resource allocation, production scheduling, transportation planning and diet formulation, ensuring the best possible outcome within given limits and project management.

Also called linear optimisation (UK spelling), linear optimization (US spelling), and sometimes referred to simply as LP or linear programming model, these terms all point to the same mathematical framework.

Core topics include problem formulation (defining variables, objective, constraints), graphical methods for two-variable cases, the Simplex Method, duality theory, sensitivity and post‑optimality analysis, and integer extensions like Mixed Integer Linear Programming (MILP). Network flow problems—transportation, assignment, transshipment—and parametric programming are key. Real‑life examples: routing delivery trucks, blending fuels, scheduling shifts. Advanced studies cover large‑scale LP, decomposition techniques (Dantzig‑Wolfe), column generation and interior‑point algorithms.

In 1939 Leonid Kantorovich in the USSR formulated resource allocation models. In 1941 T.C. Koopmans and Philip Hitchcock tackled transportation costs. George Dantzig’s 1947 Simplex Method made solving large systems practical. John von Neumann’s duality theory emerged in 1947 too. N.Z. Khachian proved polynomial‑time solvability in 1979 via the ellipsoid method. Then Narendra Karmarkar’s 1984 interior‑point algorithm spurred modern large‑scale optimzation. These early works laid the foundation for all modern LP methods and it have enabled many industries to flourish.

Do you want to learn Linear Programming? MEB has personalized one-on-one online Linear Programming tutoring just for you. We help school, college, and university students with homework, lab reports, tests, projects, essays, and dissertations—any time, day or night.

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Linear Programming stands out because it turns real‑life decisions—like budgeting time or resources—into clear math problems. You pick a goal, such as maximizing profit or minimizing cost, and list limits on materials or hours. With basic algebra and simple rules, it builds a neat system that computers solve fast, making it a core part of Operations Research for students in exams or software projects.

Compared with other courses, Linear Programming offers exact answers and uses easy‑to‑read tables or graphs. Fast solvers handle large cases, so students see real results quickly. On the downside, it needs every relationship to be straight‑line, or linear, which oversimplifies many real problems. It also works best with precise data, unlike softer subjects like statistics or qualitative studies that admit more uncertainty and complexity.

After learning linear programming, students can take up higher courses like a master’s in operations research, applied mathematics or industrial engineering, or even pursue a PhD in optimization. There are also short online programs and certifications in algorithms, data science and optimization techniques.

Linear programming skills lead to jobs such as operations researcher, supply chain analyst, data scientist and consultant. In these roles, you build mathematical models to allocate resources, optimize costs and support decision making. Work often involves coding, data analysis and presenting results to teams and clients.

Studying linear programming teaches you how to use limited resources in the best way. It is a key topic in test preparation for engineering exams like GATE, GRE quantitative sections and business school entry tests. It also builds strong logical thinking and problem‑solving skills.

Linear programming is widely used in logistics for route planning, in factories for production scheduling, in finance for portfolio management and in energy for balancing supply and demand. Its main benefits are saving time and money, improving efficiency and giving clear insight into complex trade‑offs.

Start with the basics: understand what linear equations and inequalities are. Then learn how to set up an objective function and constraints for a real problem. Practice solving simple two-variable examples graphically. Next, study the Simplex method step by step, working through small problems by hand. Use software like Excel Solver or free tools (LP Solve) to check your work. Finally, tackle more complex, multi-variable problems and real case studies to build confidence.

Linear Programming can seem tough at first because it uses algebra and optimization ideas together. However, once you get the basic steps—formulating the problem, choosing a method (graphical or Simplex), and interpreting results—it becomes much clearer. Regular practice with simple problems builds understanding. With patience and guided exercises, most students find LP manageable rather than extremely hard.

You can definitely learn Linear Programming on your own if you have good study materials and a clear plan. Start with free online tutorials and solve lots of practice questions. If you find specific steps confusing or want faster progress, working with a tutor can help you clear doubts, get personalized examples, and stay motivated. It’s your choice based on your learning style and schedule.

At MEB, we offer 24/7 online one‑on‑one tutoring in Linear Programming and other Operations Research topics. Our expert tutors guide you through each step, from problem formulation to software tools, and give instant feedback on your work. We also help with assignments and exam prep, providing clear explanations, practice problems, and tips to improve your scores.

The time needed depends on your background and goals. If you know basic algebra, you can grasp the fundamentals in about 10–15 hours of focused study. Expect to spend another 15–20 hours practicing the Simplex method and software tools. Plan a total of 30–40 hours over a few weeks for a solid foundation and to prepare for exams or assignments.

Useful resources include: YouTube channels like Khan Academy’s Linear Programming series, MIT OpenCourseWare lectures, and Professor Kulkarni’s videos. Websites: https://mathinsight.org/linear_programming, https://tutorialspoint.com/operations_research/linear_programming.htm, https://lpsolve.sourceforge.net/5.5/, and https://www.geeksforgeeks.org/linear-programming-problems-introduction/. Online courses: Coursera’s “Discrete Optimization”, edX “MicroMasters in Operations Research”. Books: “Introduction to Operations Research” by Hillier & Lieberman, “Linear Programming: Foundations and Extensions” by Vanderbei, and “Operations Research” by Taha. Software tutorials: LINGO, CPLEX, Gurobi free student editions. Forums like OR StackExchange and Reddit’s r/operationsresearch.

College students, parents, and tutors in the USA, Canada, UK and Gulf can reach out anytime. If you need a helping hand—whether online 1:1 24/7 tutoring or assignment help—our tutors at MEB can assist at an affordable fee.