minimax theorem - vertaling naar russisch
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minimax theorem - vertaling naar russisch

THEOREM PROVIDING CONDITIONS THAT GUARANTEE THAT THE MAX–MIN INEQUALITY IS ALSO AN EQUALITY
Von Neumman's minimax theorem
  • 1=''f''(''x'', ''y'') = ''y''<sup>2</sup> − ''x''<sup>2</sup>}} is concave-convex.

minimax theorem         
т. игр теорема о минимаксе
minimax theorem         

['minimæks'θiərəm]

математика

теорема о минимаксе

minimax test         
  • An animated pedagogical example that attempts to be human-friendly by substituting initial infinite (or arbitrarily large) values for emptiness and by avoiding using the [[negamax]] coding simplifications.
DECISION RULE USED IN ARTIFICIAL INTELLIGENCE, DECISION THEORY, GAME THEORY, STATISTICS AND PHILOSOPHY FOR MINIMIZING THE POSSIBLE LOSS FOR A WORST CASE (MAXIMUM LOSS) SCENARIO
Minimax algorithm; Minimax test; Maximin principle; Maximin criterion; Minmax; Maximin (decision theory); Bottleneck programming; Minimax strategy; Game value; Maximin (philosophy); Minimax principle; Maxmin; MiniMax; Minimax Strategy; Minimax solution; Minmax algorithm; Maximin Principle

математика

минимаксный критерий

Definitie

theorem
n.
Proposition (to be demonstrated), position, dictum, thesis.

Wikipedia

Minimax theorem

In the mathematical area of game theory, a minimax theorem is a theorem providing conditions that guarantee that the max–min inequality is also an equality. The first theorem in this sense is von Neumann's minimax theorem about zero-sum games published in 1928, which was considered the starting point of game theory. Von Neumann is quoted as saying "As far as I can see, there could be no theory of games ... without that theorem ... I thought there was nothing worth publishing until the Minimax Theorem was proved".

Since then, several generalizations and alternative versions of von Neumann's original theorem have appeared in the literature.

Formally, von Neumann's minimax theorem states:

Let X R n {\displaystyle X\subset \mathbb {R} ^{n}} and Y R m {\displaystyle Y\subset \mathbb {R} ^{m}} be compact convex sets. If f : X × Y R {\displaystyle f:X\times Y\rightarrow \mathbb {R} } is a continuous function that is concave-convex, i.e.

f ( , y ) : X R {\displaystyle f(\cdot ,y):X\to \mathbb {R} } is concave for fixed y {\displaystyle y} , and
f ( x , ) : Y R {\displaystyle f(x,\cdot ):Y\to \mathbb {R} } is convex for fixed x {\displaystyle x} .

Then we have that

max x X min y Y f ( x , y ) = min y Y max x X f ( x , y ) . {\displaystyle \max _{x\in X}\min _{y\in Y}f(x,y)=\min _{y\in Y}\max _{x\in X}f(x,y).}
Vertaling van &#39minimax theorem&#39 naar Russisch