Function which is convex in a variable cannot achieve maximum?












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This question arose out of studying "nice" conditions on Lagrangians. Let $L : mathbb{R}^n times mathbb{R}^n times mathbb{R} to mathbb{R}$ (let us write $L(x, v, t)$) be convex in $v$ (for any $x, t$). Is it true that any extrema of $L$ must be either global minima, or a saddle point? In particular, $L$ cannot achieve a maximum?



I ask this because the Euler-Lagrange equations are equivalent to being a critical point of the action functional, but generally we want to minimize (at least, as far as I know) the action, and so I'm wondering under what conditions we get critical point $implies$ minimum. I've seen convexity in the $v$-variable as an assumption before. Why in this variable? What about $x$, or $t$?



Many thanks for any help!










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  • $begingroup$
    I slightly updated my answer to account for trivial cases.
    $endgroup$
    – LinAlg
    Dec 21 '18 at 19:35
















1












$begingroup$


This question arose out of studying "nice" conditions on Lagrangians. Let $L : mathbb{R}^n times mathbb{R}^n times mathbb{R} to mathbb{R}$ (let us write $L(x, v, t)$) be convex in $v$ (for any $x, t$). Is it true that any extrema of $L$ must be either global minima, or a saddle point? In particular, $L$ cannot achieve a maximum?



I ask this because the Euler-Lagrange equations are equivalent to being a critical point of the action functional, but generally we want to minimize (at least, as far as I know) the action, and so I'm wondering under what conditions we get critical point $implies$ minimum. I've seen convexity in the $v$-variable as an assumption before. Why in this variable? What about $x$, or $t$?



Many thanks for any help!










share|cite|improve this question









$endgroup$












  • $begingroup$
    I slightly updated my answer to account for trivial cases.
    $endgroup$
    – LinAlg
    Dec 21 '18 at 19:35














1












1








1





$begingroup$


This question arose out of studying "nice" conditions on Lagrangians. Let $L : mathbb{R}^n times mathbb{R}^n times mathbb{R} to mathbb{R}$ (let us write $L(x, v, t)$) be convex in $v$ (for any $x, t$). Is it true that any extrema of $L$ must be either global minima, or a saddle point? In particular, $L$ cannot achieve a maximum?



I ask this because the Euler-Lagrange equations are equivalent to being a critical point of the action functional, but generally we want to minimize (at least, as far as I know) the action, and so I'm wondering under what conditions we get critical point $implies$ minimum. I've seen convexity in the $v$-variable as an assumption before. Why in this variable? What about $x$, or $t$?



Many thanks for any help!










share|cite|improve this question









$endgroup$




This question arose out of studying "nice" conditions on Lagrangians. Let $L : mathbb{R}^n times mathbb{R}^n times mathbb{R} to mathbb{R}$ (let us write $L(x, v, t)$) be convex in $v$ (for any $x, t$). Is it true that any extrema of $L$ must be either global minima, or a saddle point? In particular, $L$ cannot achieve a maximum?



I ask this because the Euler-Lagrange equations are equivalent to being a critical point of the action functional, but generally we want to minimize (at least, as far as I know) the action, and so I'm wondering under what conditions we get critical point $implies$ minimum. I've seen convexity in the $v$-variable as an assumption before. Why in this variable? What about $x$, or $t$?



Many thanks for any help!







analysis dynamical-systems euler-lagrange-equation






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asked Dec 21 '18 at 19:04









ChrisChris

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1,590413












  • $begingroup$
    I slightly updated my answer to account for trivial cases.
    $endgroup$
    – LinAlg
    Dec 21 '18 at 19:35


















  • $begingroup$
    I slightly updated my answer to account for trivial cases.
    $endgroup$
    – LinAlg
    Dec 21 '18 at 19:35
















$begingroup$
I slightly updated my answer to account for trivial cases.
$endgroup$
– LinAlg
Dec 21 '18 at 19:35




$begingroup$
I slightly updated my answer to account for trivial cases.
$endgroup$
– LinAlg
Dec 21 '18 at 19:35










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The answer to your question is negative. If $L(x,v,t)$ has a maximum at $(x^*,v^*,t^*)$, then the convex function $f(t) = (x^*,v^*,t)$ has a maximum at $t^*$, which a convex function with domain $mathbb{R}$ cannot have (unless the function is constant).






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    $begingroup$

    The answer to your question is negative. If $L(x,v,t)$ has a maximum at $(x^*,v^*,t^*)$, then the convex function $f(t) = (x^*,v^*,t)$ has a maximum at $t^*$, which a convex function with domain $mathbb{R}$ cannot have (unless the function is constant).






    share|cite|improve this answer











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      1












      $begingroup$

      The answer to your question is negative. If $L(x,v,t)$ has a maximum at $(x^*,v^*,t^*)$, then the convex function $f(t) = (x^*,v^*,t)$ has a maximum at $t^*$, which a convex function with domain $mathbb{R}$ cannot have (unless the function is constant).






      share|cite|improve this answer











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        $begingroup$

        The answer to your question is negative. If $L(x,v,t)$ has a maximum at $(x^*,v^*,t^*)$, then the convex function $f(t) = (x^*,v^*,t)$ has a maximum at $t^*$, which a convex function with domain $mathbb{R}$ cannot have (unless the function is constant).






        share|cite|improve this answer











        $endgroup$



        The answer to your question is negative. If $L(x,v,t)$ has a maximum at $(x^*,v^*,t^*)$, then the convex function $f(t) = (x^*,v^*,t)$ has a maximum at $t^*$, which a convex function with domain $mathbb{R}$ cannot have (unless the function is constant).







        share|cite|improve this answer














        share|cite|improve this answer



        share|cite|improve this answer








        edited Dec 21 '18 at 19:34

























        answered Dec 21 '18 at 19:14









        LinAlgLinAlg

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