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Hilbert basis (linear programming)

Hilbert basis (linear programming) is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Hilbert basis (linear programming) rather than just read about it. In short: The Hilbert basis of a convex cone C is a minimal set of integer vectors in C such that every integer vector in C is a conical combination of the vectors in the Hilbert basis with integer coefficients. Definition Given a lattice L ⊂ Z d {\displaystyle L\subset \mathbb {Z} ^{d}} and a convex polyhedral cone with generators a 1 , … , a n ∈ Z d {\displaystyle a_{1},\ldots ,a_{n}\in \mathbb {Z} ^{d}} C = { λ 1 a 1 + … +…

Hilbert basis (linear programming) — main illustration
Hilbert basis (linear programming) — illustration

Key takeaways

  • Hilbert basis (linear programming) belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Hilbert basis (linear programming) to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Hilbert basis (linear programming) from memory before moving on to harder problems.

Reference excerpt

The Hilbert basis of a convex cone C is a minimal set of integer vectors in C such that every integer vector in C is a conical combination of the vectors in the Hilbert basis with integer coefficients.

Definition

Given a lattice L ⊂ Z d {\displaystyle L\subset \mathbb {Z} ^{d}} and a convex polyhedral cone with generators a 1 , … , a n ∈ Z d {\displaystyle a_{1},\ldots ,a_{n}\in \mathbb {Z} ^{d}}

C = { λ 1 a 1 + … + λ n a n ∣ λ 1 , … , λ n ≥ 0 , λ 1 , … , λ n ∈ R } ⊂ R d , {\displaystyle C=\{\lambda _{1}a_{1}+\ldots +\lambda _{n}a_{n}\mid \lambda _{1},\ldots ,\lambda _{n}\geq 0,\lambda _{1},\ldots ,\lambda _{n}\in \mathbb {R} \}\subset \mathbb {R} ^{d},}

we consider the monoid C ∩ L {\displaystyle C\cap L} . By Gordan's lemma, this monoid is finitely generated, i.e., there exists a finite set of lattice points { x 1 , … , x m } ⊂ C ∩ L {\displaystyle \{x_{1},\ldots ,x_{m}\}\subset C\cap L} such that every lattice point x ∈ C ∩ L {\displaystyle x\in C\cap L} is an integer conical combination of these points:

x = λ 1 x 1 + … + λ m x m , λ 1 , … , λ m ∈ Z , λ 1 , … , λ m ≥ 0. {\displaystyle x=\lambda _{1}x_{1}+\ldots +\lambda _{m}x_{m},\quad \lambda _{1},\ldots ,\lambda _{m}\in \mathbb {Z} ,\lambda _{1},\ldots ,\lambda _{m}\geq 0.}

The cone C is called pointed if x , − x ∈ C {\displaystyle x,-x\in C} implies x = 0 {\displaystyle x=0} . In this case there exists a unique minimal generating set of the monoid C ∩ L {\displaystyle C\cap L} —the Hilbert basis of C. It is given by the set of irreducible lattice points: An element x ∈ C ∩ L {\displaystyle x\in C\cap L} is called irreducible if it can not be written as the sum of two non-zero elements, i.e., x = y + z {\displaystyle x=y+z} implies y = 0 {\displaystyle y=0} or z = 0 {\displaystyle z=0} .

References Bruns, Winfried; Gubeladze, Joseph; Henk, Martin; Martin, Alexander; Weismantel, Robert (1999), "A counterexample to an integer analogue of Carathéodory's theorem", Journal für die reine und angewandte Mathematik, 1999 (510): 179–185, doi:10.1515/crll.1999.045 Cook, William John; Fonlupt, Jean; Schrijver, Alexander (1986), "An integer analogue of Carathéodory's theorem", Journal of Combinatorial Theory, Series B, 40 (1): 63–70, doi:10.1016/0095-8956(86)90064-X Eisenbrand, Friedrich; Shmonin, Gennady (2006), "Carathéodory bounds for integer cones", Operations Research Letters, 34 (5): 564–568, doi:10.1016/j.orl.2005.09.008 D. V. Pasechnik (2001). "On computing the Hilbert bases via the Elliott—MacMahon algorithm". Theoretical Computer Science. 263 (1–2): 37–46. doi:10.1016/S0304-3975(00)00229-2. hdl:10220/8240.

Worked examples

Example 1 — a first encounter with Hilbert basis (linear programming)

Start with the simplest possible case. Write down what Hilbert basis (linear programming) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Hilbert basis (linear programming) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Hilbert basis (linear programming) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Hilbert basis (linear programming)

In research
Hilbert basis (linear programming) appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Hilbert basis (linear programming) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Hilbert basis (linear programming) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Applied mathematics stubs, Discrete geometry, Linear programming, so understanding it makes those chapters shorter.
In everyday life
Look for Hilbert basis (linear programming) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Hilbert basis (linear programming) in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Hilbert basis (linear programming) means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Hilbert basis (linear programming) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Hilbert basis (linear programming) in simple terms?

The Hilbert basis of a convex cone C is a minimal set of integer vectors in C such that every integer vector in C is a conical combination of the vectors in the Hilbert basis with integer coefficients. Definition Given a lattice L ⊂ Z d {\displaystyle L\subset \mathbb {Z} ^{d}} and a convex polyhed…

Why does Hilbert basis (linear programming) matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Hilbert basis (linear programming)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Hilbert basis (linear programming).

Tags

  • Applied mathematics stubs
  • Discrete geometry
  • Linear programming

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