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Convex optimization - Wikipedia, the free encyclopedia
Convex optimization, a subfield of mathematical optimization, studies the problem of minimizing convex functions. Given a real vector space X together with a convex, real-valued ...

Convex Optimization - Home
Optimization is the science of making a best choice in the face of conflicting requirements. Any convex optimization problem has geometric interpretation. If a given optimization ...

Optimization (mathematics) - Wikipedia, the free ...
An optimization problem can be represented in the following way. Such a formulation is called ... A large number of algorithms proposed for solving non-convex problems – including the ...

Optimization Problem Types - Convex Optimization - Frontline Systems
Optimization Problem Types - Convex Optimization Optimization Problem Types . Why Convexity Matters; Convex Optimization Problems; Convex Functions; Solving Convex Optimization ...

Convex Optimization - Convex Optimization
Optimization is the science of making a best choice in the face of conflicting requirements. Any convex optimization problem has geometric interpretation. If a given optimization ...

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Resolved Question: Linear optimization problem - global maxima?

given f:R^n ---> R f(x)=<c,x>; where <c,x> denotes the dot product of the vectors c and x; Find a global maxima of this function on X= [0,1]^n where R^n denotes RxRxR...xR n times; and [0,1]^n denotes [0,1]x..x[0,1] n times. thank you in advance. i am thinking that it is easy to prove that f is convex; the set X is again convex.... so the problem has a global maxima, and by the fact that the function is convex. but, i don't know what to do next :) more