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Linear programming

On this page
  1. Mathematical statement
  2. Representative formula (exact TeX)
  3. Assumptions
  4. Symbol scope
  5. Intuition, definition, and misconceptions
  6. Adopted derivation guide
  7. Prerequisites
  8. Verification boundary
  9. Next obligation
  10. Same-source structured data

This English edition is an AI candidate and does not yet have an OCME Atlas (GPT-6) Twin closure receipt.

Linear programming optimizes a linear objective over a feasible domain defined by linear equalities or inequalities; this exercise slice checks only a complete explicit finite candidate grid.

mko-linear-programming · definition · v0.10.0

Mathematical statement

A linear program consists of a linear objective and linear constraints and seeks a minimum or maximum on the feasible domain. A finite candidate grid permits complete checking of its listed points, but unless coverage of the general feasible domain has been proved, its conclusion is only best among the supplied candidates.

Representative formula (exact TeX)

L(x,y)

Assumptions

  • Exercises use two-variable integer constraints a*x+b*y≤c, integer-coefficient objectives and complete explicitly supplied finite candidates.
  • Finite-grid enumeration does not establish a global optimum on a continuous polyhedron or for a general linear program.

Symbol scope

  • L — linear-program data
  • x — first decision coordinate
  • y — second decision coordinate

Intuition, definition, and misconceptions

Check feasibility constraint by constraint before comparing objective values among feasible candidates.

A linear objective can tie at several candidate points; argmin or argmax must retain every optimal index.

The candidate table’s completeness is part of the conclusion’s scope. Without a polyhedral vertex-completeness proof, do not extrapolate beyond it.

Do not choose an infeasible point for its better objective value or pass off a finite-grid best point as a general LP optimum.

Adopted derivation guide

Linear programming: the best supplied candidate, and why it is not the continuous optimum

The original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.

Prerequisites

Verification boundary

Only optimality among the supplied finite candidates is established; general feasible domains, duality and optimality certificates are not developed.

not_formalized

This does not mean the content is false; it means no replayable computational or formal evidence is registered yet.

Next obligation

Add standard forms, vertices, duality and replayable optimality certificates.

Same-source structured data

This page and the interactive view use the same object and existing language edition; this adds no proof or human review.

Canonical MKO JSON · Plain Markdown