Linear Optimization Studio Simplex • Dual Simplex • Visualization

Solve LPs in matrix form, explore Simplex iterations, and visualize 2D feasible regions.

Maximize cᵀx Ax ≤ b, x ≥ 0 Educational Prototype
Problem Setup
Define dimensions and fill A, b, c
Objective: ζ = cᵀ x
Standard form: max cᵀx subject to Ax ≤ b, x ≥ 0. Slack variables are added automatically. Simplex + matrix dictionaries (B, B⁻¹, x_B, dual y) are computed from this data.
Solution & Visualization
Run Simplex, Dual Simplex & inspect dictionaries
Ready.

Optimal Solution

Optimal Objective Value
Decision Variables (x*)
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Variable Details

Slack/Surplus Variables
Basic Variables (in basis)
Basic Solution Values (x_B*)
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Dual Information

Dual Solution (Shadow Prices) y*
Basis Set 𝔅
📖 Matrix form: ζ-row and basic rows
🔍 Tableaux and pivot operations
Plot is only meaningful when n = 2 and constraints are Ax ≤ b with x ≥ 0.