Convex optimization game theory

  • What is game theory in optimization techniques?

    Game theory is a branch of applied mathematics that provides tools for analyzing situations in which parties ( called players) make decisions that are interdependent.
    This interdependence causes each player to consider the other player's possible decisions( or strategies) in formulating strategy..

  • What is the theory of game optimization?

    In other words, game theory is a collection of analytical tools that can be used to make optimal choices in interactional and decision making problems.
    Optimization in mathematics and computer science is the choice of the best member of an existing collection for a specific purpose..

Jan 15, 2015The importance of VI: that they provide a theory in which to test existance/uniqueness of solutions, and algorithms to find those solutions!!

Scholarly articles for convex optimization game theory

scholar.google.com › citationsConvex optimization, game theory, and variational …
ScutariCited by 389
Game theory and convex optimization methods in …
MoklyachukCited by 47
Convex optimization theory
BertsekasCited by 1079
Jan 15, 2015The importance of VI: that they provide a theory in which to test existance/uniqueness of solutions, and algorithms to find those solutions!!

Is discrete convex analysis a tool for Economics and game theory?

This paper presents discrete convex analysis as a tool for economics and game theory

Discrete convex analysis is a new framework of discrete mathematics and optimization, developed during the last two decades

Recently, it is being recognized as a powerful tool for analyzing economic or game models with indivisibilities

Lexicographic optimization is a kind of Multi-objective optimization.
In general, multi-objective optimization deals with optimization problems with two or more objective functions to be optimized simultaneously.
Often, the different objectives can be ranked in order of importance to the decision-maker, so that objective mwe-math-element> is the most important, objective mwe-math-element> is the next most important, and so on.
Lexicographic optimization presumes that the decision-maker prefers even a very small increase in mwe-math-element>, to even a very large increase in mwe-math-element> etc.
Similarly, the decision-maker prefers even a very small increase in mwe-math-element>, to even a very large increase in mwe-math-element
> etc.
In other words, the decision-maker has lexicographic preferences, ranking the possible solutions according to a lexicographic order of their objective function values.
Lexicographic optimization is sometimes called preemptive optimization, since a small increase in one objective value preempts a much larger increase in less important objective values.

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Convex hull optimization
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Online convex optimization hazan