{
  "index": "1988-A-6",
  "type": "ALG",
  "tag": [
    "ALG"
  ],
  "difficulty": "",
  "question": "If a linear transformation $A$ on an $n$-dimensional vector space has\n$n+1$ eigenvectors such that any $n$ of them are linearly independent,\ndoes it follow that $A$ is a scalar multiple of the identity? Prove\nyour answer.",
  "solution": "Solution 1. Let \\( x_{1}, x_{2}, \\ldots, x_{n+1} \\) be the given eigenvectors, and let \\( \\lambda_{1} \\), \\( \\lambda_{2}, \\ldots, \\lambda_{n+1} \\) be their eigenvalues. The set \\( B_{i}=\\left\\{x_{1}, \\ldots, x_{i-1}, x_{i+1}, \\ldots, x_{n+1}\\right\\} \\) is a linearly independent set of \\( n \\) vectors in an \\( n \\)-dimensional vector space, so \\( B_{i} \\) is a basis, with respect to which \\( A \\) is represented by the diagonal matrix \\( \\operatorname{diag}\\left(\\lambda_{1}, \\ldots, \\lambda_{i-1}, \\lambda_{i+1}, \\ldots, \\lambda_{n+1}\\right) \\). Thus the trace of \\( A \\) equals \\( S-\\lambda_{i} \\) where \\( S= \\) \\( \\sum_{i=1}^{n+1} \\lambda_{i} \\). But the trace is independent of the basis chosen, so \\( S-\\lambda_{i}=S-\\lambda_{j} \\) for all \\( i, j \\). Hence all the \\( \\lambda_{i} \\) are equal. With respect to the basis \\( B_{1}, A \\) is represented by a diagonal matrix with equal entries on the diagonal, so \\( A \\) is a scalar multiple of the identity.\n\nRemark. One could have worked with the multiset of roots of the characteristic polynomial, instead of their sum (the trace).\n\nSolution 2 (Lenny \\( \\mathbf{N g} \\) ). Let \\( x_{1}, \\ldots, x_{n+1} \\) be the eigenvectors of \\( A \\), with eigenvalues \\( \\lambda_{1}, \\ldots, \\lambda_{n+1} \\). Since \\( x_{1}, \\ldots, x_{n} \\) are linearly independent, they span the vector space; hence\n\\[\nx_{n+1}=\\sum_{i=1}^{n} \\alpha_{i} x_{i}\n\\]\nfor some \\( \\alpha_{1}, \\ldots, \\alpha_{n} \\). Multiply by \\( \\lambda_{n+1} \\), or apply \\( A \\) to both sides, and compare:\n\\[\n\\sum_{i=1}^{n} \\alpha_{i} \\lambda_{n+1} x_{i}=\\lambda_{n+1} x_{n+1}=\\sum_{i=1}^{n} \\alpha_{i} \\lambda_{i} x_{i}\n\\]\n\nThus \\( \\alpha_{i} \\lambda_{n+1}=\\alpha_{i} \\lambda_{i} \\) for all \\( i \\) between 1 and \\( n \\). If \\( \\alpha_{i}=0 \\) for some \\( i \\), then \\( x_{n+1} \\) can be expressed as a linear combination of \\( x_{1}, \\ldots, x_{i-1}, x_{i+1}, \\ldots, x_{n} \\), contradicting the linear independence hypothesis. Hence \\( \\alpha_{i} \\neq 0 \\) for all \\( i \\), so \\( \\lambda_{n+1}=\\lambda_{i} \\) for all \\( i \\). This implies \\( A=\\lambda_{n+1} I \\).",
  "vars": [
    "x",
    "x_1",
    "x_2",
    "x_n+1",
    "x_i",
    "x_i-1",
    "\\\\lambda",
    "\\\\lambda_1",
    "\\\\lambda_2",
    "\\\\lambda_n+1",
    "\\\\lambda_i"
  ],
  "params": [
    "A",
    "n",
    "B_i",
    "S",
    "\\\\alpha_i",
    "i",
    "j",
    "I"
  ],
  "sci_consts": [],
  "variants": {
    "descriptive_long": {
      "map": {
        "A": "linearop",
        "n": "spacedim",
        "B_i": "basisindex",
        "B_{i}": "basisindex",
        "B_{1}": "basisone",
        "S": "eigsums",
        "\\alpha_i": "coefalpha",
        "\\alpha_{i}": "coefalpha",
        "\\alpha_{1}": "coefalphaone",
        "\\alpha_{n}": "coefalphalast",
        "i": "indexone",
        "j": "indextwo",
        "I": "identity",
        "x": "genericvector",
        "x_1": "vectorone",
        "x_{1}": "vectorone",
        "x_2": "vectortwo",
        "x_{2}": "vectortwo",
        "x_n+1": "vectornplus",
        "x_{n+1}": "vectornplus",
        "x_n": "vectorn",
        "x_{n}": "vectorn",
        "x_i": "vectorindex",
        "x_{i}": "vectorindex",
        "x_i-1": "vectorprev",
        "x_{i-1}": "vectorprev",
        "x_{i+1}": "vectornext",
        "\\lambda": "eigval",
        "\\lambda_1": "eigvalone",
        "\\lambda_{1}": "eigvalone",
        "\\lambda_2": "eigvaltwo",
        "\\lambda_{2}": "eigvaltwo",
        "\\lambda_n+1": "eigvalnplus",
        "\\lambda_{n+1}": "eigvalnplus",
        "\\lambda_i": "eigvalindex",
        "\\lambda_{i}": "eigvalindex",
        "\\lambda_{i-1}": "eigvalprev",
        "\\lambda_{i+1}": "eigvalnext",
        "\\lambda_j": "eigvalsecond",
        "\\lambda_{j}": "eigvalsecond"
      },
      "question": "If a linear transformation $linearop$ on an $spacedim$-dimensional vector space has\n$spacedim+1$ eigenvectors such that any $spacedim$ of them are linearly independent,\ndoes it follow that $linearop$ is a scalar multiple of the identity? Prove\nyour answer.",
      "solution": "Solution 1. Let \\( vectorone, vectortwo, \\ldots, vectornplus \\) be the given eigenvectors, and let \\( eigvalone, eigvaltwo, \\ldots, eigvalnplus \\) be their eigenvalues. The set \\( basisindex=\\left\\{vectorone, \\ldots, vectorprev, vectornext, \\ldots, vectornplus\\right\\} \\) is a linearly independent set of \\( spacedim \\) vectors in an \\( spacedim \\)-dimensional vector space, so \\( basisindex \\) is a basis, with respect to which \\( linearop \\) is represented by the diagonal matrix \\( \\operatorname{diag}\\left(eigvalone, \\ldots, eigvalprev, eigvalnext, \\ldots, eigvalnplus\\right) \\). Thus the trace of \\( linearop \\) equals \\( eigsums-eigvalindex \\) where \\( eigsums=\\sum_{indexone=1}^{spacedim+1} eigvalindex \\). But the trace is independent of the basis chosen, so \\( eigsums-eigvalindex=eigsums-eigvalsecond \\) for all \\( indexone, indextwo \\). Hence all the \\( eigvalindex \\) are equal. With respect to the basis \\( basisone \\), \\( linearop \\) is represented by a diagonal matrix with equal entries on the diagonal, so \\( linearop \\) is a scalar multiple of the identity.\n\nRemark. One could have worked with the multiset of roots of the characteristic polynomial, instead of their sum (the trace).\n\nSolution 2 (Lenny \\( \\mathbf{N g} \\) ). Let \\( vectorone, \\ldots, vectornplus \\) be the eigenvectors of \\( linearop \\), with eigenvalues \\( eigvalone, \\ldots, eigvalnplus \\). Since \\( vectorone, \\ldots, vectorn \\) are linearly independent, they span the vector space; hence\n\\[\nvectornplus=\\sum_{indexone=1}^{spacedim} coefalpha vectorindex\n\\]\nfor some coefficients \\( coefalphaone, \\ldots, coefalphalast \\). Multiply by \\( eigvalnplus \\), or apply \\( linearop \\) to both sides, and compare:\n\\[\n\\sum_{indexone=1}^{spacedim} coefalpha\\, eigvalnplus\\, vectorindex\n= eigvalnplus\\, vectornplus\n= \\sum_{indexone=1}^{spacedim} coefalpha\\, eigvalindex\\, vectorindex .\n\\]\n\nThus \\( coefalpha\\, eigvalnplus = coefalpha\\, eigvalindex \\) for all \\( indexone \\) between 1 and \\( spacedim \\). If \\( coefalpha = 0 \\) for some \\( indexone \\), then \\( vectornplus \\) can be expressed as a linear combination of \\( vectorone, \\ldots, vectorprev, vectornext, \\ldots, vectorn \\), contradicting the linear independence hypothesis. Hence \\( coefalpha \\neq 0 \\) for all \\( indexone \\), so \\( eigvalnplus=eigvalindex \\) for all \\( indexone \\). This implies \\( linearop=eigvalnplus\\, identity \\)."
    },
    "descriptive_long_confusing": {
      "map": {
        "A": "marigold",
        "n": "chameleon",
        "B_i": "brassflute",
        "S": "dragonfly",
        "\\alpha_i": "blueberry",
        "i": "thunderpig",
        "j": "gingerroot",
        "I": "sandpaper",
        "x": "moonlight",
        "x_1": "football",
        "x_2": "horseback",
        "x_n+1": "cottoncnd",
        "x_i": "seashells",
        "x_i-1": "jellybean",
        "\\lambda": "paintbrush",
        "\\lambda_1": "toothbrush",
        "\\lambda_2": "paperclips",
        "\\lambda_n+1": "harmonicas",
        "\\lambda_i": "cinnamon"
      },
      "question": "If a linear transformation $marigold$ on an $chameleon$-dimensional vector space has\n$chameleon+1$ eigenvectors such that any $chameleon$ of them are linearly independent,\ndoes it follow that $marigold$ is a scalar multiple of the identity? Prove\nyour answer.",
      "solution": "Solution 1. Let \\( football, horseback, \\ldots, cottoncnd \\) be the given eigenvectors, and let \\( toothbrush \\), \\( paperclips, \\ldots, harmonicas \\) be their eigenvalues. The set \\( brassflute=\\left\\{football, \\ldots, jellybean, moonlight_{thunderpig+1}, \\ldots, cottoncnd\\right\\} \\) is a linearly independent set of \\( chameleon \\) vectors in an \\( chameleon \\)-dimensional vector space, so \\( brassflute \\) is a basis, with respect to which \\( marigold \\) is represented by the diagonal matrix \\( \\operatorname{diag}\\left(toothbrush, \\ldots, paintbrush_{thunderpig-1}, paintbrush_{thunderpig+1}, \\ldots, harmonicas\\right) \\). Thus the trace of \\( marigold \\) equals \\( dragonfly-paintbrush_{thunderpig} \\) where \\( dragonfly= \\) \\( \\sum_{thunderpig=1}^{chameleon+1} paintbrush_{thunderpig} \\). But the trace is independent of the basis chosen, so \\( dragonfly-paintbrush_{thunderpig}=dragonfly-paintbrush_{gingerroot} \\) for all \\( thunderpig, gingerroot \\). Hence all the \\( paintbrush_{thunderpig} \\) are equal. With respect to the basis \\( B_{1}, marigold \\) is represented by a diagonal matrix with equal entries on the diagonal, so \\( marigold \\) is a scalar multiple of the identity.\n\nRemark. One could have worked with the multiset of roots of the characteristic polynomial, instead of their sum (the trace).\n\nSolution 2 (Lenny \\( \\mathbf{N g} \\) ). Let \\( football, horseback, \\ldots, cottoncnd \\) be the eigenvectors of \\( marigold \\), with eigenvalues \\( toothbrush, paperclips, \\ldots, harmonicas \\). Since \\( football, \\ldots, moonlight_{chameleon} \\) are linearly independent, they span the vector space; hence\n\\[\ncottoncnd=\\sum_{thunderpig=1}^{chameleon} blueberry_{thunderpig} seashells\n\\]\nfor some \\( blueberry_{1}, \\ldots, blueberry_{chameleon} \\). Multiply by \\( harmonicas \\), or apply \\( marigold \\) to both sides, and compare:\n\\[\n\\sum_{thunderpig=1}^{chameleon} blueberry_{thunderpig} harmonicas seashells=\\harmonicas cottoncnd=\\sum_{thunderpig=1}^{chameleon} blueberry_{thunderpig} cinnamon seashells\n\\]\n\nThus \\( blueberry_{thunderpig} harmonicas=blueberry_{thunderpig} cinnamon \\) for all \\( thunderpig \\) between 1 and \\( chameleon \\). If \\( blueberry_{thunderpig}=0 \\) for some \\( thunderpig \\), then \\( cottoncnd \\) can be expressed as a linear combination of \\( football, \\ldots, moonlight_{thunderpig-1}, moonlight_{thunderpig+1}, \\ldots, moonlight_{chameleon} \\), contradicting the linear independence hypothesis. Hence \\( blueberry_{thunderpig} \\neq 0 \\) for all \\( thunderpig \\), so \\( harmonicas=cinnamon \\) for all \\( thunderpig \\). This implies \\( marigold=harmonicas sandpaper \\)."
    },
    "descriptive_long_misleading": {
      "map": {
        "x": "fixedscalar",
        "x_1": "fixedscalarone",
        "x_2": "fixedscalartwo",
        "x_n+1": "fixedscalarnplus",
        "x_i": "fixedscalarindex",
        "x_i-1": "fixedscalarprev",
        "\\\\lambda": "orthovector",
        "\\\\lambda_1": "orthovectorone",
        "\\\\lambda_2": "orthovectortwo",
        "\\\\lambda_n+1": "orthovectornplus",
        "\\\\lambda_i": "orthovectorindex",
        "A": "staticmatrix",
        "n": "infinitevalue",
        "B_i": "nonbasisindex",
        "S": "productvalue",
        "\\\\alpha_i": "unknownfactor",
        "i": "totality",
        "j": "partiality",
        "I": "zeromatrix"
      },
      "question": "If a linear transformation staticmatrix on an infinitevalue-dimensional vector space has infinitevalue+1 eigenvectors such that any infinitevalue of them are linearly independent, does it follow that staticmatrix is a scalar multiple of the identity? Prove your answer.",
      "solution": "Solution 1. Let \\( fixedscalarone, fixedscalartwo, \\ldots, fixedscalarnplus \\) be the given eigenvectors, and let \\( orthovectorone \\), \\( orthovectortwo, \\ldots, orthovectornplus \\) be their eigenvalues. The set \\( nonbasisindex=\\left\\{fixedscalarone, \\ldots, fixedscalarprev, x_{i+1}, \\ldots, fixedscalarnplus\\right\\} \\) is a linearly independent set of \\( infinitevalue \\) vectors in an \\( infinitevalue \\)-dimensional vector space, so \\( nonbasisindex \\) is a basis, with respect to which \\( staticmatrix \\) is represented by the diagonal matrix \\( \\operatorname{diag}\\left(orthovectorone, \\ldots, \\lambda_{i-1}, \\lambda_{i+1}, \\ldots, orthovectornplus\\right) \\). Thus the trace of \\( staticmatrix \\) equals \\( productvalue-orthovectorindex \\) where \\( productvalue= \\) \\( \\sum_{totality=1}^{infinitevalue+1} orthovectorindex \\). But the trace is independent of the basis chosen, so \\( productvalue-orthovectorindex=productvalue-\\lambda_{j} \\) for all \\( totality, partiality \\). Hence all the \\( orthovectorindex \\) are equal. With respect to the basis \\( B_{1} \\), staticmatrix is represented by a diagonal matrix with equal entries on the diagonal, so staticmatrix is a scalar multiple of the identity.\n\nRemark. One could have worked with the multiset of roots of the characteristic polynomial, instead of their sum (the trace).\n\nSolution 2 (Lenny \\( \\mathbf{N g} \\) ). Let \\( fixedscalarone, \\ldots, fixedscalarnplus \\) be the eigenvectors of staticmatrix, with eigenvalues \\( orthovectorone, \\ldots, orthovectornplus \\). Since \\( fixedscalarone, \\ldots, x_{n} \\) are linearly independent, they span the vector space; hence\n\\[\nfixedscalarnplus=\\sum_{totality=1}^{infinitevalue} unknownfactor fixedscalarindex\n\\]\nfor some unknownfactor. Multiply by orthovectornplus, or apply staticmatrix to both sides, and compare:\n\\[\n\\sum_{totality=1}^{infinitevalue} unknownfactor orthovectornplus fixedscalarindex=orthovectornplus fixedscalarnplus=\\sum_{totality=1}^{infinitevalue} unknownfactor orthovectorindex fixedscalarindex\n\\]\n\nThus \\( unknownfactor orthovectornplus=unknownfactor orthovectorindex \\) for all totality between 1 and infinitevalue. If \\( unknownfactor=0 \\) for some totality, then fixedscalarnplus can be expressed as a linear combination of fixedscalarone, \\ldots, x_{i-1}, x_{i+1}, \\ldots, x_{n}, contradicting the linear independence hypothesis. Hence \\( unknownfactor \\neq 0 \\) for all totality, so \\( orthovectornplus=orthovectorindex \\) for all totality. This implies \\( staticmatrix=orthovectornplus\\,zeromatrix \\)."
    },
    "garbled_string": {
      "map": {
        "A": "ufizqemr",
        "n": "galbshok",
        "B_i": "yjtrnspq",
        "B_{i}": "yjtrnspq",
        "S": "kvhoudcz",
        "\\alpha_i": "weqkmtbn",
        "\\alpha_{i}": "weqkmtbn",
        "j": "vxqplrse",
        "I": "odlhfgaz",
        "x": "qzxwvtnp",
        "x_1": "hjgrksla",
        "x_{1}": "hjgrksla",
        "x_2": "mofpdqei",
        "x_{2}": "mofpdqei",
        "x_n+1": "zlbtrvwy",
        "x_{n+1}": "zlbtrvwy",
        "x_i": "karpsufm",
        "x_{i}": "karpsufm",
        "x_i-1": "ydnceago",
        "x_{i-1}": "ydnceago",
        "\\lambda": "sbvxhjui",
        "\\lambda_1": "pqlmztgc",
        "\\lambda_{1}": "pqlmztgc",
        "\\lambda_2": "rksdoafw",
        "\\lambda_{2}": "rksdoafw",
        "\\lambda_n+1": "tnhgwebv",
        "\\lambda_{n+1}": "tnhgwebv",
        "\\lambda_i": "cuvzsalp",
        "\\lambda_{i}": "cuvzsalp"
      },
      "question": "If a linear transformation $ufizqemr$ on an $galbshok$-dimensional vector space has $galbshok+1$ eigenvectors such that any $galbshok$ of them are linearly independent, does it follow that $ufizqemr$ is a scalar multiple of the identity? Prove your answer.",
      "solution": "Solution 1. Let \\( hjgrksla, mofpdqei, \\ldots, zlbtrvwy \\) be the given eigenvectors, and let \\( pqlmztgc, rksdoafw, \\ldots, tnhgwebv \\) be their eigenvalues. The set \\( yjtrnspq=\\left\\{hjgrksla, \\ldots, ydnceago, x_{i+1}, \\ldots, zlbtrvwy\\right\\} \\) is a linearly independent set of \\( galbshok \\) vectors in an \\( galbshok \\)-dimensional vector space, so \\( yjtrnspq \\) is a basis, with respect to which \\( ufizqemr \\) is represented by the diagonal matrix \\( \\operatorname{diag}\\left(pqlmztgc, \\ldots, \\lambda_{i-1}, \\lambda_{i+1}, \\ldots, tnhgwebv\\right) \\). Thus the trace of \\( ufizqemr \\) equals \\( kvhoudcz-cuvzsalp \\) where \\( kvhoudcz= \\sum_{i=1}^{galbshok+1} cuvzsalp \\). But the trace is independent of the basis chosen, so \\( kvhoudcz-cuvzsalp=kvhoudcz-\\lambda_{vxqplrse} \\) for all \\( i, vxqplrse \\). Hence all the \\( cuvzsalp \\) are equal. With respect to the basis \\( B_{1}, ufizqemr \\) is represented by a diagonal matrix with equal entries on the diagonal, so \\( ufizqemr \\) is a scalar multiple of the identity.\n\nSolution 2 (Lenny \\( \\mathbf{N g} \\)). Let \\( hjgrksla, \\ldots, zlbtrvwy \\) be the eigenvectors of \\( ufizqemr \\), with eigenvalues \\( pqlmztgc, \\ldots, tnhgwebv \\). Since \\( hjgrksla, \\ldots, x_{n} \\) are linearly independent, they span the vector space; hence\n\\[\nzlbtrvwy=\\sum_{i=1}^{galbshok} weqkmtbn karpsufm\n\\]\nfor some \\( \\alpha_{1}, \\ldots, \\alpha_{galbshok} \\). Multiply by \\( tnhgwebv \\), or apply \\( ufizqemr \\) to both sides, and compare:\n\\[\n\\sum_{i=1}^{galbshok} weqkmtbn tnhgwebv karpsufm=tnhgwebv zlbtrvwy=\\sum_{i=1}^{galbshok} weqkmtbn cuvzsalp karpsufm\n\\]\nThus \\( weqkmtbn tnhgwebv=weqkmtbn cuvzsalp \\) for all \\( i \\) between 1 and \\( galbshok \\). If \\( weqkmtbn=0 \\) for some \\( i \\), then \\( zlbtrvwy \\) can be expressed as a linear combination of \\( hjgrksla, \\ldots, x_{i-1}, x_{i+1}, \\ldots, x_{n} \\), contradicting the linear independence hypothesis. Hence \\( weqkmtbn \\neq 0 \\) for all \\( i \\); consequently, \\( tnhgwebv=cuvzsalp \\) for all \\( i \\). This implies \\( ufizqemr=tnhgwebv odlhfgaz \\)."
    },
    "kernel_variant": {
      "question": "Let $V$ be an $n$–dimensional vector space over an algebraically closed\nfield $\\mathbb K$ of characteristic $0$, where $n\\ge 3$, and let\n$T\\in\\operatorname{End}(V)$.  \n\nFor $k=1,\\dots ,n$ denote by $\\wedge^{k}V$ the $k$–th exterior power of\n$V$, and by $\\wedge^{k}T$ the linear map induced by $T$ on\n$\\wedge^{k}V$.  \n\nAssume that there exists a set  \n\\[\nE=\\{v_{1},\\dots ,v_{n+1}\\}\\subset V\\qquad(\\text{all }v_{i}\\neq 0)\n\\]\nwith the following properties:\n\n(1)  (over–complete independence) every $n$–element subset of $E$ is a\nbasis of $V$;\n\n(2)  (non–degenerate two–fold eigen–wedge condition)  \n     for every pair of distinct indices $1\\le i<j\\le n+1$ the simple\n     $2$–vector $v_{i}\\wedge v_{j}$ is an eigenvector of\n     $\\wedge^{2}T$ with a {\\em non–zero} eigenvalue, i.e.\n\\[\n\\wedge^{2}T\\bigl(v_{i}\\wedge v_{j}\\bigr)=\\lambda_{ij}\\,\n        (v_{i}\\wedge v_{j}),\\qquad \\lambda_{ij}\\in\\mathbb K^{\\times}.\n\\]\n\n(a) Prove that $T$ is a scalar multiple of the identity operator on\n$V$.\n\n(b) Conversely, show that if \n\\[\nT=\\lambda\\,\\operatorname{Id}_{V}\\qquad(\\lambda\\in\\mathbb K^{\\times})\n\\]\nthen property {\\rm(2)} is automatically satisfied with\n$\\lambda_{ij}=\\lambda^{2}$.",
      "solution": "Throughout fix the rank–$2$ subspaces\n\\[\nW_{ij}:=\\operatorname{span}\\{v_{i},v_{j}\\}\\qquad(1\\le i<j\\le n+1).\n\\]\n\nStep 1 – every plane $W_{ij}$ is $T$–invariant.  \nChoose a complement $U$ of $W_{ij}$ in $V$, so\n$V=W_{ij}\\oplus U$.  Decompose\n\\[\nT v_{i}=u+u',\\qquad T v_{j}=w+w',\n\\]\nwith $u,w\\in W_{ij}$ and $u',w'\\in U$.  Expanding in $\\wedge^{2}V$ we\nobtain  \n\\[\nT v_{i}\\wedge T v_{j}=(u+u')\\wedge(w+w')\n                      =u\\wedge w+u\\wedge w'+u'\\wedge w+u'\\wedge w'.\n\\tag{1}\n\\]\n\nBecause $\\wedge^{2}V=\\wedge^{2}W_{ij}\\;\\oplus\\;(W_{ij}\\wedge U)\\;\\oplus\\;\\wedge^{2}U$,\nthe three mixed terms\n$u\\wedge w'$, $u'\\wedge w$, $u'\\wedge w'$ in (1) lie in the last two\nsummands, whereas $u\\wedge w\\in\\wedge^{2}W_{ij}$.  On the other hand\nthe hypothesis yields\n\\[\n\\wedge^{2}T\\bigl(v_{i}\\wedge v_{j}\\bigr)=\\lambda_{ij}(v_{i}\\wedge v_{j})\n          \\in\\wedge^{2}W_{ij}\\setminus\\{0\\}.\n\\]\nHence all components of (1) outside $\\wedge^{2}W_{ij}$ must vanish:\n\\[\nu\\wedge w'=0,\\qquad u'\\wedge w=0,\\qquad u'\\wedge w'=0.\n\\tag{2}\n\\]\n\nWe now show that $u'=w'=0$.  \nSuppose $u'\\neq 0$.  Because $w\\in W_{ij}\\setminus\\{0\\}$ (if $w=0$ then\n$T v_{j}=w'\\in U$ and $T v_{i}=u+u'$ with $u\\neq 0$, so\n$u\\wedge w'\\neq 0$, contradicting $u\\wedge w'=0$), the second equality\nin (2) implies $u'\\wedge w=0$, which forces $u'$ to be a scalar multiple\nof $w$.  But $u'\\in U$ and $w\\in W_{ij}$, while\n$U\\cap W_{ij}=\\{0\\}$, so $u'=0$, a contradiction.  Thus $u'=0$.  The same\nargument applied to $w'$ gives $w'=0$.  Therefore  \n\\[\nT v_{i},\\,T v_{j}\\in W_{ij},\n\\]\nand $W_{ij}$ is $T$–stable.\n\nStep 2 – every triple of vectors in $E$ is independent.  \nIf $v_{p},v_{q},v_{r}$ were dependent, they could be completed to an\n$n$–element subset of $E$, contradicting (1).  Consequently, for fixed\n$i$ and distinct $j,k$,\n\\[\nW_{ij}\\cap W_{ik}=\\operatorname{span}\\{v_{i}\\}.\n\\tag{3}\n\\]\n\nStep 3 – each $v_{i}$ is an eigenvector of $T$.  \nFix $i$ and pick $j,k\\neq i$.  By Step~1,\n$T v_{i}\\in W_{ij}$ and $T v_{i}\\in W_{ik}$; by (3) this forces\n\\[\nT v_{i}=\\mu_{i}\\,v_{i}\\qquad(\\mu_{i}\\in\\mathbb K).\n\\tag{4}\n\\]\n\nStep 4 – all eigenvalues coincide.  \nFor each $i$ set $B_{i}:=E\\setminus\\{v_{i}\\}$.  By (1) the $n$ vectors\nin $B_{i}$ form a basis consisting of eigenvectors, so in that basis\n\\[\n\\operatorname{Mat}_{B_{i}}(T)=\n\\operatorname{diag}(\\mu_{1},\\dots ,\\mu_{i-1},\\mu_{i+1},\\dots ,\\mu_{n+1}).\n\\]\nSince the trace is basis–independent (and the field has characteristic\n$0$),\n\\[\n\\sum_{\\ell\\neq p}\\mu_{\\ell}=\\operatorname{tr}T=\\sum_{\\ell\\neq q}\\mu_{\\ell}\n      \\quad\\Longrightarrow\\quad\\mu_{p}=\\mu_{q},\n\\]\nfor all $p,q$.  Hence $\\mu_{1}=\\dots=\\mu_{n+1}=:\\lambda$.\n\nStep 5 – conclusion.  \nBecause $\\{v_{1},\\dots ,v_{n}\\}$ is a basis of $V$, the matrix of $T$\nin that basis is $\\lambda\\,\\operatorname{Id}_{n}$, i.e.\n\\[\nT=\\lambda\\,\\operatorname{Id}_{V}.\n\\]\n\nConversely, if $T=\\lambda\\,\\operatorname{Id}_{V}$ with\n$\\lambda\\in\\mathbb K^{\\times}$, then\n\\[\n\\wedge^{2}T=\\lambda^{2}\\operatorname{Id}_{\\wedge^{2}V},\n\\]\nand every $2$–vector (hence every $v_{i}\\wedge v_{j}$) is an eigenvector\nwith eigenvalue $\\lambda^{2}\\in\\mathbb K^{\\times}$.  Therefore property\n{\\rm(2)} is satisfied with $\\lambda_{ij}=\\lambda^{2}$, completing the\nconverse.",
      "metadata": {
        "replaced_from": "harder_variant",
        "replacement_date": "2025-07-14T19:09:31.705452",
        "was_fixed": false,
        "difficulty_analysis": "•  Higher-dimensional algebra:  The problem no longer refers only to V but to every exterior power ∧^kV, whose dimensions are binomial coefficients and whose operators ∧^kT encode all elementary symmetric polynomials of the eigenvalues.  \n•  Multiple simultaneous conditions:  Eigen-wedge property (A) must hold for *every* k = 1,…,n-1, and the combinatorial over-independence (B) must hold in *each* of those spaces.  The solver must juggle these interacting constraints.  \n•  Use of advanced structures:  Exterior algebras, induced representations (∧^kT), decomposable vs. indecomposable k-vectors, and determinants all come into play; none of them appear in the original exercise.  \n•  Deeper theoretical insight:  One must understand how eigenvalues of T propagate to eigenvalues of ∧^kT (they behave as k-fold products) and exploit the 1-dimensionality of ∧^nV to equate those products, an argument well beyond simple trace computations.  \n•  More steps:  The solution requires establishing basis properties, passing to highest exterior powers, cancelling products of eigenvalues, and finally arguing about diagonalisation—substantially lengthier than the original one-or-two-line trace argument.\n\nConsequently the enhanced variant demands broader algebraic knowledge, more intricate reasoning, and a multi-stage proof, making it significantly harder than both the original problem and the previous kernel variant."
      }
    },
    "original_kernel_variant": {
      "question": "Let V be an n–dimensional vector space (n ≥ 3) over an algebraically closed\nfield 𝕂 of characteristic 0 and let  \n\n  T ∈ End(V).\n\nFor k = 1,…,n write ∧ᵏV for the k-th exterior power of V and let ∧ᵏT be the\ninduced linear map on ∧ᵏV.\n\nAssume that T admits a set  \n\n  E = {v₁,…,v_{n+1}} ⊂ V  (|E| = n+1)  \n\nof non-zero vectors satisfying\n\n(A) (2–fold eigen-wedge condition)  \n For every distinct i,j one has  \n\n  ∧²T(v_i∧v_j)=λ_{ij}(v_i∧v_j) for some λ_{ij} ∈ 𝕂.\n\n(B) (Overcomplete independence in V)  \n Every n–element subset of E is a basis of V.\n\nProve that T is a scalar multiple of the identity operator on V.\n\n(Observe that condition (A) involves only the exterior square ∧²T; no\nassumption is made that the v_i themselves are eigenvectors of T.\nShowing that they must be, and that their eigenvalues coincide, is the\ncore of the problem.)\n\n--------------------------------------------------------------------",
      "solution": "Write  \n\n W_{ij}:=span{v_i,v_j}  (1≤i<j≤n+1).\n\nStep 1. Each plane W_{ij} is T–invariant.  \nBecause ∧²T acts on decomposable 2–vectors via\n  ∧²T(v_i∧v_j)=T v_i ∧ T v_j,\ncondition (A) says that T v_i ∧ T v_j is a scalar multiple of v_i ∧ v_j.\nIf, say, T v_i had a component outside W_{ij}, write\n  T v_i = u + u′  \nwith u ∈ W_{ij}, u′ ∉ W_{ij}.  Then\n\n  T v_i ∧ T v_j = (u+u′) ∧ T v_j = u∧T v_j + u′∧T v_j.\n\nThe second summand u′∧T v_j lies outside v_i∧v_j’s line, so the equality\nwith λ_{ij}(v_i∧v_j) is impossible unless u′=0.  The same argument with\nthe roles of i and j exchanged shows T v_j∈W_{ij}.  Hence W_{ij} is\nT-stable.\n\nStep 2. Any three vectors of E are linearly independent.  \nAssume v_r,v_s,v_t are dependent.  Complete them with n−3 further\nvectors of E to obtain an n–element subset of E; by (B) that subset\nshould be a basis, contradicting the dependence.  Thus every triple is\nindependent, and in particular\n\n  W_{ij}∩W_{ik} = span{v_i}  (i fixed, j,k distinct). (2)\n\nStep 3. Each v_i is an eigenvector of T.  \nFix i and choose j,k distinct from i.  By Step 1, T v_i lies both in\nW_{ij} and W_{ik}; by (2) their intersection is span{v_i}.  Therefore\n  T v_i = μ_i v_i for some μ_i ∈ 𝕂. (3)\n\nStep 4. The eigenvalues μ₁,…,μ_{n+1} are equal.  \nFor each i set  \n\n  B_i := E \\ {v_i} = {v_1,…,v_{i-1},v_{i+1},…,v_{n+1}}.\n\nBy (B) the n vectors of B_i form a basis of V, and by (3) they are all\neigenvectors of T.  Hence the matrix of T in the basis B_i is the\ndiagonal matrix diag(μ_1,…,μ_{i-1},μ_{i+1},…,μ_{n+1}), so\n\n  tr T = Σ_{ℓ≠ i} μ_ℓ  (independent of i). (4)\n\nChoose p≠q.  Comparing the two equal expressions for tr T coming from\ni=p and i=q gives Σ_{ℓ≠ p} μ_ℓ = Σ_{ℓ≠ q} μ_ℓ, whence μ_p = μ_q.\nSince p,q were arbitrary, all μ_i coincide; write μ_i=λ for every i.\n\nStep 5. Conclusion.  \nBecause {v_1,…,v_n} is a basis of V consisting of eigenvectors with the\ncommon eigenvalue λ, the matrix of T in that basis is λ·Id_n, so\n\n  T = λ·Id_V.\n\n--------------------------------------------------------------------",
      "metadata": {
        "replaced_from": "harder_variant",
        "replacement_date": "2025-07-14T01:37:45.550653",
        "was_fixed": false,
        "difficulty_analysis": "•  Higher-dimensional algebra:  The problem no longer refers only to V but to every exterior power ∧^kV, whose dimensions are binomial coefficients and whose operators ∧^kT encode all elementary symmetric polynomials of the eigenvalues.  \n•  Multiple simultaneous conditions:  Eigen-wedge property (A) must hold for *every* k = 1,…,n-1, and the combinatorial over-independence (B) must hold in *each* of those spaces.  The solver must juggle these interacting constraints.  \n•  Use of advanced structures:  Exterior algebras, induced representations (∧^kT), decomposable vs. indecomposable k-vectors, and determinants all come into play; none of them appear in the original exercise.  \n•  Deeper theoretical insight:  One must understand how eigenvalues of T propagate to eigenvalues of ∧^kT (they behave as k-fold products) and exploit the 1-dimensionality of ∧^nV to equate those products, an argument well beyond simple trace computations.  \n•  More steps:  The solution requires establishing basis properties, passing to highest exterior powers, cancelling products of eigenvalues, and finally arguing about diagonalisation—substantially lengthier than the original one-or-two-line trace argument.\n\nConsequently the enhanced variant demands broader algebraic knowledge, more intricate reasoning, and a multi-stage proof, making it significantly harder than both the original problem and the previous kernel variant."
      }
    }
  },
  "checked": true,
  "problem_type": "proof"
}