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Linear Programming
  Solves a Linear Programming problem in canonical form by Dantzig's simplex method.
2024.Jul.03 18:17:45
iOpt Maximization or minimization.
n No. of variables (structural, slack, artificial). •
PT Objective function coefficients •
A Constraint matrix   (row, then RHS of equality  <ret> new row …) •
Initial basis Variables' indices •
Show Show intermediate steps: bases (b), reduced costs (rc), matrices (m).
  Solves a Linear Programming problem in "canonical" form, i.e., with equations only and xi ≥ 0.
  The constraint matrix, A, must be given ending (each row) with the right-hand side (RHS) constant ('return' at end of line). So, e.g., − x1 + 4 x2 ≤ 78 would become  −1  4 … 78 . The program finds the number of constraints.
  This Problem follows the manual resolution by the matrix method (revised simplex). For a "commercial" resolution: NAG version.
  'Delta' is: (a) [V. Tavares, 1996] the reduced cost (rc) vector; (b) [WinQSB, 1996] the rc vector for the structural basic variables, and minus the shadow prices for the constraints, according to the slack variables. ('Lindo' [2002] gives symmetrical rc.)
References:

• Tavares, L. Valadares, Rui Carvalho Oliveira, Isabel Hall Themido, F. Nunes Correia, 1996, "Investigação Operacional" (Operational Research), McGraw-Hill, Amadora (Portugal).

• WinQSB ↓ (see instructions !) by Yih-Long Chang in Lawrence, Jr., John A. and Barry A. Pasternack, 2.nd ed., 2002, "Applied Management Science: modeling, spreadsheet analysis, and communication for decision making", John Wiley, New York, NY (USA).

• Lindo ↓, Lindo Systems, Inc., Chicago, IL (USA).

• Wagner, Harvey M., 1972+, "Principles of Operations Research, with applications to managerial decisions", John Wiley, New York, NY (USA).

 
 
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Created: 1999-08-23 — Last modified: 2009-03-14