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  "body": "\n## Definition\n\nLet $m, n \\ge 1$ and let $f \\colon \\R^{m \\times n} \\to \\R$.\nFor $a \\in \\R^m$ and $b \\in \\R^n$, write $a \\otimes b$ for the $m \\times n$ matrix with entries $a_i b_\\alpha$; it has [[rank|0ZC9YTXZRY]] at most one.\nThe function $f$ is *rank-one convex* if for every $\\xi \\in \\R^{m \\times n}$, $a \\in \\R^m$ and $b \\in \\R^n$ the function\n$$t \\mapsto f(\\xi + t \\, a \\otimes b), \\quad t \\in \\R,$$\nis [[convex|07GR3TNW4H]].\n\n## Remarks\n\nExample: for $m = n = 2$, $f = \\det$ is rank-one convex.\nBy the matrix determinant lemma, $\\det(\\xi + t \\, a \\otimes b) = \\det \\xi + t \\, b^T \\operatorname{adj}(\\xi) \\, a$, which is affine in $t$.\nBut $\\det$ is not convex: along $\\xi(t) = \\operatorname{diag}(t, -t)$ it equals $-t^2$.\n\nNon-example: for $m = n = 1$, $f(\\xi) = -\\xi^2$ is not rank-one convex, since $t \\mapsto -t^2$ is not convex.\n\nFor $m = 1$ or $n = 1$ every matrix has rank at most one, so rank-one convexity is ordinary convexity.\nIf $f$ is $C^2$, it is rank-one convex exactly when $\\sum_{i,j,\\alpha,\\beta} \\frac{\\partial^2 f}{\\partial \\xi^i_\\alpha \\partial \\xi^j_\\beta}(\\xi) \\, a_i a_j b_\\alpha b_\\beta \\ge 0$ for all $\\xi$, $a$ and $b$, the Legendre–Hadamard condition.\n",
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