Udemy

Free Linear Programming Course: Constraints and Feasible Regions

A resource limit becomes a mathematical constraint when it is written precisely. Sandeep Kumar Mathur teaches introductory linear programming through graphical examples, feasible regions and the conversion of a production word problem into a model.

Free Linear Programming Course: Constraints and Feasible Regions — Sandeep Kumar Mathur. WikiFree selection.
Free learning access

Listed as Free on Udemy when checked on 22 October 2026. No coupon or subscription trial is required for the listed lessons. Enrollment needs a Udemy account; availability can change.

About this course

A resource limit becomes a mathematical constraint when it is written precisely. Sandeep Kumar Mathur teaches introductory linear programming through graphical examples, feasible regions and the conversion of a production word problem into a model.

The course has five lectures in two sections and 42 minutes of recorded material. Most of that time is spent working through examples, with a short introductory entry and a quiz supporting the problem-solving sequence.

Official course Operation Research : A course on Linear Programming Problems
Created by Sandeep Kumar Mathur
Platform Udemy
Language English
Level All Levels — Udemy listing
Listed duration 42 minutes; 5 lectures across 2 sections
Certificate Not included with a new free enrollment

Skills you’ll gain

  • Translating simple resource limits into inequalities
  • Plotting constraints and identifying a feasible region
  • Evaluating an objective at feasible corner points
  • Explaining non-negativity constraints in a production model

Elementary mathematics is the stated requirement. Learners should be comfortable rearranging a linear equation and plotting a line; revisit those skills if the first example feels too fast.

The documented lessons focus on graphical linear programming. References in the description to possible future game theory, PERT/CPM or broader operations research topics are not evidence that those subjects are currently taught here.

Paper, a ruler and a calculator are sufficient for the introductory graphical exercises; no optimization software purchase is required. As an additional learning check, explain what each variable represents and why a proposed point satisfies every constraint before calculating its objective value.

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Choose your next step

Refresh the algebra: Algebra foundations for equations and graphs. Review lines and inequalities before optimization.

Broaden quantitative reasoning: Business statistics and interpretation. Add tools for understanding uncertain data alongside deterministic constraints.

Frequently asked questions

Does this cover the simplex algorithm?

The published outline documents graphical examples, not a full simplex-method course.

Are future topics part of the current lessons?

Only the listed linear-programming lessons are counted here.

Do I need a paid solver?

No solver purchase is required for the graphical exercises described.

Checked on 22 October 2026 using the public course details and syllabus. WikiFree has not watched every lesson or tested full playback. New free enrollments do not include a Udemy completion certificate, instructor Q&A or direct messages.

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