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Linear Programming basics

Number 1 linear programming course. Learn the simplex method, duality and sensitivity analysis for linear programs

  1. Topics
  2. Teaching & Academics
  3. Linear Programming

Linear Programming basics

InstructorKrunal Patel
Duration3h 6m
Students13.8K
Rating4.7 (101)
Sponsored
Price
$14.99
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Comidoc Analysis

Theoretical foundations of the Simplex method and Duality

Strengths

Rigorous Duality Coverage

The curriculum provides depth in duality theory, covering weak and strong duality, primal-dual feasibility, and Farkas' lemma.

Systematic Problem Solving

Instruction follows a structured progression from basic simplex intuition to more advanced revised simplex techniques and sensitivity analysis.

Limitations

Limited Instructional Detail

One signal indicates that some explanations may lack sufficient depth.

Best suited to

  • Students of mathematics or optimization
  • Professionals needing theoretical foundations

Less suited to

  • Learners seeking practical optimization modeling
  • Those requiring programming-based implementation

Comidoc Score

7.0/10

Recommended
Intermediate level

Comidoc verdict

Mathematical proofs and the mechanics of optimization form the core of this instruction, moving from basic simplex methods to complex duality theory and sensitivity analysis. This theoretical focus is supported by distributed written assignments designed to test comprehension of underlying mathematical principles.

The curriculum excels at explaining how changes in constraints affect solutions through sensitivity analysis. However, the instruction does not extend into modeling optimization problems, which remains a separate discipline.

This course is best suited for students or professionals who require a rigorous mathematical understanding of linear programming theory rather than practical model construction.

Score breakdown

Curriculum depth
8.8

The curriculum demonstrates significant depth in mathematical concepts such as duality and the simplex method.

Applied learning
7.3

Learning is reinforced through four distributed written assignments and practical problem-solving examples.

Clarity & experience
5.0

While many signals point to a step-by-step approach, one signal suggests the explanations may lack detail.

Currency & reliability
5.0

The available evidence does not establish enough about current reliability to move this dimension away from neutral.

Audience fit
7.3

The curriculum aligns with the requirement for a linear algebra background and targets optimization students.

More Related Topics

  • Simplex Method6
  • Duality Theory2
  • Sensitivity Analysis9
  • Optimization Algorithms81
  • Operations Research22
  • Mathematical Optimization46
  • Transportation Problem5
  • Nonlinear Algorithms8
  • Queuing Theory5
  • Optimization Solvers6