{
  "index": "1963-B-6",
  "type": "GEO",
  "tag": [
    "GEO",
    "ALG"
  ],
  "difficulty": "",
  "question": "6. Let \\( E \\) be a Euclidean space of at most three dimensions. If \\( A \\) is a nonempty subset of \\( E \\), define \\( S(A) \\) to be the set of all points that lie on closed segments joining pairs of points of \\( A \\). For a given nonempty set \\( A_{0} \\), define \\( A_{n} \\equiv S\\left(A_{n-1}\\right) \\) for \\( n=1,2, \\ldots \\). Prove that \\( A_{2}=A_{3}=\\cdots \\). (A one-point set should be considered to be a special case of a closed segment.)",
  "solution": "Solution. We consider \\( E \\) as a vector space as usual. If \\( a_{1}, a_{2}, \\ldots, a_{n} \\) are in \\( E \\), then any point (vector) \\( p \\) which can be written in the form\n\\[\np=\\sum_{i=1}^{n} \\lambda_{i} a_{i}\n\\]\nwhere \\( \\lambda_{i} \\geq 0 \\) and \\( \\Sigma \\lambda_{i}=1 \\), is called a convex combination of the \\( a \\) 's.\nConvex combination is transitive; that is, if \\( b_{1}, b_{2}, \\ldots, b_{j} \\) are each convex combinations of \\( a_{1}, a_{2}, \\ldots, a_{n} \\), and \\( c \\) is a convex combination of \\( b_{1}, b_{2}, \\ldots, b_{j} \\), then \\( c \\) is a convex combination of \\( a_{1}, a_{2}, \\ldots, a_{n} \\). It follows that, if \\( A \\) is a given set in \\( E \\), the set \\( K \\) of all convex combinations of elements of \\( A \\) is a convex set; indeed, it is the smallest convex set containing \\( A . K \\) is called the convex hull of \\( A \\).\n\nThe essence of the problem lies in the following theorem.\nTheorem. Suppose \\( E \\) has dimension \\( n \\), and \\( A \\) is a subset of E. Then every point in the convex hull \\( K \\) of \\( A \\) can be written as a convex combination of at most \\( n+1 \\) points of \\( A \\).\n\nProof. Suppose \\( p \\in K \\), but \\( p \\) cannot be written as a convex combination of fewer than \\( n+2 \\) points of \\( A \\). Since \\( p \\in K \\), we can write\n\\[\np=\\sum_{i=1}^{q} \\lambda_{i} a_{i}\n\\]\nwhere \\( a_{1} \\in A . \\lambda_{1} \\geq 0 \\), and \\( \\Sigma \\lambda_{i}=1 \\). Of all such representations we choose one with \\( q \\) as small as possible. Then \\( q>n+1 \\).\n\nThere exist numbers \\( \\mu_{1}, \\mu_{2}, \\ldots, \\mu_{q} \\), not all zero, such that\n\\[\n\\sum_{i \\pm 1}^{q} \\mu_{i}=0\n\\]\n\\[\n\\sum_{i=1}^{4} \\mu_{i} a_{i}=0,\n\\]\nsince (2) can be regarded as a system of \\( n+1 \\) linear homogeneous equations in more than \\( n+1 \\) unknowns \\( \\mu_{i} \\).\n\nFrom (1) and (2) we have\n\\[\np=\\sum_{i=1}^{4}\\left(\\lambda_{i}+\\sigma \\mu_{i}\\right) a_{i}\n\\]\nfor any \\( \\sigma \\in \\mathbf{R} \\). Here the coefficient sum is always 1 . We can choose \\( \\sigma \\) so that one of the new coefficients \\( \\lambda_{i}+\\sigma \\mu_{i}=0 \\) while the rest are non-negative. Indeed, let \\( \\sigma \\) be the largest of the (negative) numbers \\( -\\lambda_{i} / \\mu_{i} \\), considering only \\( i \\) 's for which \\( \\mu_{i}>0 \\), of which there must be at least one. But this gives a representation of \\( p \\) as a convex combination of fewer than \\( q \\) elements of \\( A \\), which is impossible because of our choice of \\( q \\). This contradiction proves the theorem.\n\nReturning to the problem, let \\( K \\) be the convex hull of \\( A_{0} \\). If \\( X \\) is any subset of \\( K \\), then \\( X \\subseteq S(X) \\subseteq K \\). (The first inclusion requires that degenerate segments be allowed; \\( x \\) is on the \"segment\" from \\( x \\) to \\( x \\).) It follows that\n\\[\nA_{0} \\subseteq A_{1} \\subseteq A_{2} \\subseteq A_{3} \\subseteq \\cdots \\subseteq K\n\\]\n\nSuppose \\( p \\in K \\). By the theorem we can write\n\\[\np=\\lambda_{1} a_{1}+\\lambda_{2} a_{2}+\\lambda_{3} a_{3}+\\lambda_{4} a_{4},\n\\]\nwhere \\( a_{i} \\in A_{0}, \\Sigma \\lambda_{i}=1 \\), and \\( \\lambda_{i}>0 \\). (If perchance \\( p \\) can be represented as a convex combination of fewer than four points of \\( A_{0} \\), we can allow several of the \\( a_{i} \\) 's to be the same and thus arrange that all the coefficients are positive.) Put\n\\[\n\\begin{array}{l}\nq=\\frac{\\lambda_{1}}{\\lambda_{1}+\\lambda_{2}} a_{1}+\\frac{\\lambda_{2}}{\\lambda_{1}+\\lambda_{2}} a_{2} \\\\\nr=\\frac{\\lambda_{3}}{\\lambda_{3}+\\lambda_{4}} a_{3}+\\frac{\\lambda_{4}}{\\lambda_{3}+\\lambda_{4}} a_{4}\n\\end{array}\n\\]\n\nThen\n\\[\n\\begin{array}{c}\nq \\in S\\left(A_{0}\\right)=A_{1}, \\quad r \\in A_{1}, \\quad \\text { and } \\\\\np=\\left(\\lambda_{1}+\\lambda_{2}\\right) q+\\left(\\lambda_{3}+\\lambda_{4}\\right) r \\in S\\left(A_{1}\\right)=A_{2}\n\\end{array}\n\\]\n\nThis proves that \\( K \\subseteq A_{2} \\). Then from (3) it follows that\n\\[\nA_{2}=A_{3}=\\cdots=K\n\\]",
  "vars": [
    "A",
    "S",
    "A_n",
    "a_i",
    "p",
    "\\\\lambda_i",
    "b_j",
    "c",
    "K",
    "q",
    "\\\\mu_i",
    "\\\\sigma",
    "r",
    "x",
    "X"
  ],
  "params": [
    "E",
    "n",
    "A_0"
  ],
  "sci_consts": [],
  "variants": {
    "descriptive_long": {
      "map": {
        "A": "setalpha",
        "S": "segmentset",
        "A_n": "alphaseq",
        "a_i": "elementa",
        "p": "pointvec",
        "\\lambda_i": "lambdaparam",
        "b_j": "betacomb",
        "c": "centroid",
        "K": "convexhul",
        "q": "varsmallq",
        "\\mu_i": "muparam",
        "\\sigma": "sigmavar",
        "r": "varsmallr",
        "x": "genericx",
        "X": "subsetbig",
        "E": "euclidspace",
        "n": "dimension",
        "A_0": "initialset"
      },
      "question": "6. Let \\( euclidspace \\) be a Euclidean space of at most three dimensions. If \\( setalpha \\) is a nonempty subset of \\( euclidspace \\), define \\( segmentset(setalpha) \\) to be the set of all points that lie on closed segments joining pairs of points of \\( setalpha \\). For a given nonempty set \\( initialset \\), define \\( alphaseq_{dimension} \\equiv segmentset\\left(alphaseq_{dimension-1}\\right) \\) for \\( dimension=1,2, \\ldots \\). Prove that \\( alphaseq_{2}=alphaseq_{3}=\\cdots \\). (A one-point set should be considered to be a special case of a closed segment.)",
      "solution": "Solution. We consider \\( euclidspace \\) as a vector space as usual. If \\( elementa_{1}, elementa_{2}, \\ldots, elementa_{dimension} \\) are in \\( euclidspace \\), then any point (vector) \\( pointvec \\) which can be written in the form\n\\[\npointvec=\\sum_{i=1}^{dimension} lambdaparam \\, elementa_{i}\n\\]\nwhere \\( lambdaparam \\ge 0 \\) and \\( \\Sigma lambdaparam =1 \\), is called a convex combination of the elementa's.\n\nConvex combination is transitive; that is, if \\( betacomb_{1}, betacomb_{2}, \\ldots, betacomb_{j} \\) are each convex combinations of \\( elementa_{1}, elementa_{2}, \\ldots, elementa_{dimension} \\), and \\( centroid \\) is a convex combination of \\( betacomb_{1}, betacomb_{2}, \\ldots, betacomb_{j} \\), then \\( centroid \\) is a convex combination of \\( elementa_{1}, elementa_{2}, \\ldots, elementa_{dimension} \\). It follows that, if \\( setalpha \\) is a given set in \\( euclidspace \\), the set \\( convexhul \\) of all convex combinations of elements of \\( setalpha \\) is a convex set; indeed, it is the smallest convex set containing \\( setalpha. \\)  \\( convexhul \\) is called the convex hull of \\( setalpha \\).\n\nThe essence of the problem lies in the following theorem.\n\nTheorem. Suppose \\( euclidspace \\) has dimension \\( dimension \\), and \\( setalpha \\) is a subset of euclidspace. Then every point in the convex hull \\( convexhul \\) of \\( setalpha \\) can be written as a convex combination of at most \\( dimension+1 \\) points of \\( setalpha \\).\n\nProof. Suppose \\( pointvec \\in convexhul \\), but \\( pointvec \\) cannot be written as a convex combination of fewer than \\( dimension+2 \\) points of \\( setalpha \\). Since \\( pointvec \\in convexhul \\), we can write\n\\[\npointvec = \\sum_{i=1}^{varsmallq} lambdaparam \\, elementa_{i}\n\\]\nwhere \\( elementa_{1} \\in setalpha ,\\; lambdaparam \\ge 0 \\), and \\( \\Sigma lambdaparam =1 \\). Of all such representations we choose one with \\( varsmallq \\) as small as possible. Then \\( varsmallq>dimension+1 \\).\n\nThere exist numbers \\( muparam_{1}, muparam_{2}, \\ldots, muparam_{varsmallq} \\), not all zero, such that\n\\[\n\\sum_{i=1}^{varsmallq} muparam_{i}=0,\\qquad\n\\sum_{i=1}^{4} muparam_{i}\\, elementa_{i}=0,\n\\]\nsince the second relation can be regarded as a system of \\( dimension+1 \\) linear homogeneous equations in more than \\( dimension+1 \\) unknowns \\( muparam_{i} \\).\n\nFrom (1) and (2) we have\n\\[\npointvec=\\sum_{i=1}^{4}\\bigl(lambdaparam+sigmavar\\, muparam_{i}\\bigr)\\, elementa_{i}\n\\]\nfor any \\( sigmavar \\in \\mathbf{R} \\). Here the coefficient sum is always 1. We can choose \\( sigmavar \\) so that one of the new coefficients \\( lambdaparam+sigmavar\\, muparam_{i}=0 \\) while the rest are non-negative. Indeed, let \\( sigmavar \\) be the largest of the (negative) numbers \\( -lambdaparam / muparam_{i} \\), considering only indices for which \\( muparam_{i}>0 \\), of which there must be at least one. But this gives a representation of \\( pointvec \\) as a convex combination of fewer than \\( varsmallq \\) elements of \\( setalpha \\), which is impossible because of our choice of \\( varsmallq \\). This contradiction proves the theorem.\n\nReturning to the problem, let \\( convexhul \\) be the convex hull of \\( initialset \\). If \\( subsetbig \\) is any subset of \\( convexhul \\), then \\( subsetbig \\subseteq segmentset(subsetbig) \\subseteq convexhul \\). (The first inclusion requires that degenerate segments be allowed; \\( genericx \\) is on the \"segment\" from \\( genericx \\) to \\( genericx \\).) It follows that\n\\[\ninitialset \\subseteq alphaseq_{1} \\subseteq alphaseq_{2} \\subseteq alphaseq_{3} \\subseteq \\cdots \\subseteq convexhul .\n\\]\n\nSuppose \\( pointvec \\in convexhul \\). By the theorem we can write\n\\[\npointvec = lambdaparam_{1} \\, elementa_{1} + lambdaparam_{2} \\, elementa_{2} + lambdaparam_{3} \\, elementa_{3} + lambdaparam_{4} \\, elementa_{4},\n\\]\nwhere \\( elementa_{i} \\in initialset ,\\; \\Sigma lambdaparam_{i}=1, \\) and \\( lambdaparam_{i}>0 \\). (If perchance \\( pointvec \\) can be represented as a convex combination of fewer than four points of \\( initialset \\), we can allow several of the \\( elementa_{i} \\)'s to be the same and thus arrange that all the coefficients are positive.) Put\n\\[\n\\begin{array}{l}\nvarsmallq = \\dfrac{lambdaparam_{1}}{lambdaparam_{1}+lambdaparam_{2}}\\, elementa_{1} + \\dfrac{lambdaparam_{2}}{lambdaparam_{1}+lambdaparam_{2}}\\, elementa_{2},\\\\[6pt]\nvarsmallr = \\dfrac{lambdaparam_{3}}{lambdaparam_{3}+lambdaparam_{4}}\\, elementa_{3} + \\dfrac{lambdaparam_{4}}{lambdaparam_{3}+lambdaparam_{4}}\\, elementa_{4}\n\\end{array}\n\\]\nThen\n\\[\n\\begin{array}{c}\nvarsmallq \\in segmentset(initialset)=alphaseq_{1},\\quad varsmallr \\in alphaseq_{1},\\quad\\text{and}\\\\[6pt]\npointvec = (lambdaparam_{1}+lambdaparam_{2})\\, varsmallq + (lambdaparam_{3}+lambdaparam_{4})\\, varsmallr \\in segmentset(alphaseq_{1}) = alphaseq_{2}.\n\\end{array}\n\\]\nThis proves that \\( convexhul \\subseteq alphaseq_{2} \\). Together with the chain above, we get\n\\[\nalphaseq_{2} = alphaseq_{3} = \\cdots = convexhul .\n\\]\nHence \\( alphaseq_{2}=alphaseq_{3}=\\cdots \\), as desired."
    },
    "descriptive_long_confusing": {
      "map": {
        "A": "whirlwind",
        "S": "moonraker",
        "A_n": "treetopple",
        "a_i": "firemantle",
        "p": "sandcastle",
        "\\lambda_i": "dandelion",
        "b_j": "marshmallow",
        "c": "thunderbolt",
        "K": "quagmirex",
        "q": "bricklayer",
        "\\mu_i": "applecider",
        "\\sigma": "houseplant",
        "r": "starflower",
        "x": "rattlesnake",
        "X": "blacksmith",
        "E": "dragonfly",
        "n": "farmhouse",
        "A_0": "wandering"
      },
      "question": "Let \\( dragonfly \\) be a Euclidean space of at most three dimensions. If \\( whirlwind \\) is a nonempty subset of \\( dragonfly \\), define \\( moonraker(whirlwind) \\) to be the set of all points that lie on closed segments joining pairs of points of \\( whirlwind \\). For a given nonempty set \\( wandering \\), define \\( treetopple_{farmhouse} \\equiv moonraker\\left(treetopple_{farmhouse-1}\\right) \\) for \\( farmhouse=1,2, \\ldots \\). Prove that \\( treetopple_{2}=treetopple_{3}=\\cdots \\). (A one-point set should be considered to be a special case of a closed segment.)",
      "solution": "Solution. We consider \\( dragonfly \\) as a vector space as usual. If \\( firemantle_{1}, firemantle_{2}, \\ldots, firemantle_{farmhouse} \\) are in \\( dragonfly \\), then any point (vector) \\( sandcastle \\) which can be written in the form\n\\[\nsandcastle=\\sum_{i=1}^{farmhouse} dandelion_{i} firemantle_{i}\n\\]\nwhere \\( dandelion_{i} \\geq 0 \\) and \\( \\Sigma dandelion_{i}=1 \\), is called a convex combination of the \\( firemantle \\)'s.\nConvex combination is transitive; that is, if \\( marshmallow_{1}, marshmallow_{2}, \\ldots, marshmallow_{j} \\) are each convex combinations of \\( firemantle_{1}, firemantle_{2}, \\ldots, firemantle_{farmhouse} \\), and \\( thunderbolt \\) is a convex combination of \\( marshmallow_{1}, marshmallow_{2}, \\ldots, marshmallow_{j} \\), then \\( thunderbolt \\) is a convex combination of \\( firemantle_{1}, firemantle_{2}, \\ldots, firemantle_{farmhouse} \\). It follows that, if \\( whirlwind \\) is a given set in \\( dragonfly \\), the set \\( quagmirex \\) of all convex combinations of elements of \\( whirlwind \\) is a convex set; indeed, it is the smallest convex set containing \\( whirlwind . quagmirex \\) is called the convex hull of \\( whirlwind \\).\n\nThe essence of the problem lies in the following theorem.\nTheorem. Suppose \\( dragonfly \\) has dimension \\( farmhouse \\), and \\( whirlwind \\) is a subset of dragonfly. Then every point in the convex hull \\( quagmirex \\) of \\( whirlwind \\) can be written as a convex combination of at most \\( farmhouse+1 \\) points of \\( whirlwind \\).\n\nProof. Suppose \\( sandcastle \\in quagmirex \\), but \\( sandcastle \\) cannot be written as a convex combination of fewer than \\( farmhouse+2 \\) points of \\( whirlwind \\). Since \\( sandcastle \\in quagmirex \\), we can write\n\\[\nsandcastle=\\sum_{i=1}^{bricklayer} dandelion_{i} firemantle_{i}\n\\]\nwhere \\( firemantle_{1} \\in whirlwind . dandelion_{1} \\geq 0 \\), and \\( \\Sigma dandelion_{i}=1 \\). Of all such representations we choose one with \\( bricklayer \\) as small as possible. Then \\( bricklayer>farmhouse+1 \\).\n\nThere exist numbers \\( applecider_{1}, applecider_{2}, \\ldots, applecider_{bricklayer} \\), not all zero, such that\n\\[\n\\sum_{i \\pm 1}^{bricklayer} applecider_{i}=0\n\\]\n\\[\n\\sum_{i=1}^{4} applecider_{i} firemantle_{i}=0,\n\\]\nsince (2) can be regarded as a system of \\( farmhouse+1 \\) linear homogeneous equations in more than \\( farmhouse+1 \\) unknowns \\( applecider_{i} \\).\n\nFrom (1) and (2) we have\n\\[\nsandcastle=\\sum_{i=1}^{4}\\left(dandelion_{i}+houseplant\\, applecider_{i}\\right) firemantle_{i}\n\\]\nfor any \\( houseplant \\in \\mathbf{R} \\). Here the coefficient sum is always 1. We can choose \\( houseplant \\) so that one of the new coefficients \\( dandelion_{i}+houseplant\\, applecider_{i}=0 \\) while the rest are non-negative. Indeed, let \\( houseplant \\) be the largest of the (negative) numbers \\( -dandelion_{i} / applecider_{i} \\), considering only \\( i \\)'s for which \\( applecider_{i}>0 \\), of which there must be at least one. But this gives a representation of \\( sandcastle \\) as a convex combination of fewer than \\( bricklayer \\) elements of \\( whirlwind \\), which is impossible because of our choice of \\( bricklayer \\). This contradiction proves the theorem.\n\nReturning to the problem, let \\( quagmirex \\) be the convex hull of \\( wandering \\). If \\( blacksmith \\) is any subset of \\( quagmirex \\), then \\( blacksmith \\subseteq moonraker(blacksmith) \\subseteq quagmirex \\). (The first inclusion requires that degenerate segments be allowed; \\( rattlesnake \\) is on the \"segment\" from \\( rattlesnake \\) to \\( rattlesnake \\).) It follows that\n\\[\nwandering \\subseteq treetopple_{1} \\subseteq treetopple_{2} \\subseteq treetopple_{3} \\subseteq \\cdots \\subseteq quagmirex\n\\]\n\nSuppose \\( sandcastle \\in quagmirex \\). By the theorem we can write\n\\[\nsandcastle=dandelion_{1} firemantle_{1}+dandelion_{2} firemantle_{2}+dandelion_{3} firemantle_{3}+dandelion_{4} firemantle_{4},\n\\]\nwhere \\( firemantle_{i} \\in wandering, \\Sigma dandelion_{i}=1 \\), and \\( dandelion_{i}>0 \\). (If perchance \\( sandcastle \\) can be represented as a convex combination of fewer than four points of \\( wandering \\), we can allow several of the \\( firemantle_{i} \\)'s to be the same and thus arrange that all the coefficients are positive.) Put\n\\[\n\\begin{array}{l}\nbricklayer=\\frac{dandelion_{1}}{dandelion_{1}+dandelion_{2}} firemantle_{1}+\\frac{dandelion_{2}}{dandelion_{1}+dandelion_{2}} firemantle_{2} \\\\\nstarflower=\\frac{dandelion_{3}}{dandelion_{3}+dandelion_{4}} firemantle_{3}+\\frac{dandelion_{4}}{dandelion_{3}+dandelion_{4}} firemantle_{4}\n\\end{array}\n\\]\n\nThen\n\\[\n\\begin{array}{c}\nbricklayer \\in moonraker\\left(wandering\\right)=treetopple_{1}, \\quad starflower \\in treetopple_{1}, \\quad \\text { and } \\\\\nsandcastle=\\left(dandelion_{1}+dandelion_{2}\\right) bricklayer+\\left(dandelion_{3}+dandelion_{4}\\right) starflower \\in moonraker\\left(treetopple_{1}\\right)=treetopple_{2}\n\\end{array}\n\\]\n\nThis proves that \\( quagmirex \\subseteq treetopple_{2} \\). Then from (3) it follows that\n\\[\ntreetopple_{2}=treetopple_{3}=\\cdots=quagmirex\n\\]"
    },
    "descriptive_long_misleading": {
      "map": {
        "A": "emptiness",
        "S": "diverging",
        "A_n": "vacuityseq",
        "a_i": "nonpoint",
        "p": "lackness",
        "\\lambda_i": "fullvalue",
        "b_j": "voidpoint",
        "c": "nilvalue",
        "K": "concavity",
        "q": "lowpoint",
        "\\mu_i": "coarscoef",
        "\\sigma": "stability",
        "r": "endpoint",
        "x": "nullpoint",
        "X": "universe",
        "E": "noneuclid",
        "n": "flatness",
        "A_0": "vacuumbase"
      },
      "question": "<<<\n6. Let \\( noneuclid \\) be a Euclidean space of at most three dimensions. If \\( emptiness \\) is a nonempty subset of \\( noneuclid \\), define \\( diverging(emptiness) \\) to be the set of all points that lie on closed segments joining pairs of points of \\( emptiness \\). For a given nonempty set \\( vacuumbase_{0} \\), define \\( vacuityseq_{flatness} \\equiv diverging\\left(vacuityseq_{flatness-1}\\right) \\) for \\( flatness=1,2, \\ldots \\). Prove that \\( vacuityseq_{2}=vacuityseq_{3}=\\cdots \\). (A one-point set should be considered to be a special case of a closed segment.)\n>>>",
      "solution": "<<<\nSolution. We consider \\( noneuclid \\) as a vector space as usual. If \\( nonpoint_{1}, nonpoint_{2}, \\ldots, nonpoint_{flatness} \\) are in \\( noneuclid \\), then any point (vector) \\( lackness \\) which can be written in the form\n\\[\nlackness=\\sum_{i=1}^{flatness} fullvalue_{i} \\, nonpoint_{i}\n\\]\nwhere \\( fullvalue_{i} \\geq 0 \\) and \\( \\Sigma fullvalue_{i}=1 \\), is called a convex combination of the \\( nonpoint \\)'s.\nConvex combination is transitive; that is, if \\( voidpoint_{1}, voidpoint_{2}, \\ldots, voidpoint_{j} \\) are each convex combinations of \\( nonpoint_{1}, nonpoint_{2}, \\ldots, nonpoint_{flatness} \\), and \\( nilvalue \\) is a convex combination of \\( voidpoint_{1}, voidpoint_{2}, \\ldots, voidpoint_{j} \\), then \\( nilvalue \\) is a convex combination of \\( nonpoint_{1}, nonpoint_{2}, \\ldots, nonpoint_{flatness} \\). It follows that, if \\( emptiness \\) is a given set in \\( noneuclid \\), the set \\( concavity \\) of all convex combinations of elements of \\( emptiness \\) is a convex set; indeed, it is the smallest convex set containing \\( emptiness . concavity \\) is called the convex hull of \\( emptiness \\).\n\nThe essence of the problem lies in the following theorem.\nTheorem. Suppose \\( noneuclid \\) has dimension \\( flatness \\), and \\( emptiness \\) is a subset of noneuclid. Then every point in the convex hull \\( concavity \\) of \\( emptiness \\) can be written as a convex combination of at most \\( flatness+1 \\) points of \\( emptiness \\).\n\nProof. Suppose \\( lackness \\in concavity \\), but \\( lackness \\) cannot be written as a convex combination of fewer than \\( flatness+2 \\) points of \\( emptiness \\). Since \\( lackness \\in concavity \\), we can write\n\\[\nlackness=\\sum_{i=1}^{q} fullvalue_{i}\\, nonpoint_{i}\n\\]\nwhere \\( nonpoint_{1} \\in emptiness ,\\  fullvalue_{1} \\geq 0 \\), and \\( \\Sigma fullvalue_{i}=1 \\). Of all such representations we choose one with \\( q \\) as small as possible. Then \\( q>flatness+1 \\).\n\nThere exist numbers \\( coarscoef_{1}, coarscoef_{2}, \\ldots, coarscoef_{q} \\), not all zero, such that\n\\[\n\\sum_{i \\pm 1}^{q} coarscoef_{i}=0\n\\]\n\\[\n\\sum_{i=1}^{4} coarscoef_{i}\\, nonpoint_{i}=0,\n\\]\nsince (2) can be regarded as a system of \\( flatness+1 \\) linear homogeneous equations in more than \\( flatness+1 \\) unknowns \\( coarscoef_{i} \\).\n\nFrom (1) and (2) we have\n\\[\nlackness=\\sum_{i=1}^{4}\\left(fullvalue_{i}+stability \\, coarscoef_{i}\\right) nonpoint_{i}\n\\]\nfor any \\( stability \\in \\mathbf{R} \\). Here the coefficient sum is always 1. We can choose \\( stability \\) so that one of the new coefficients \\( fullvalue_{i}+stability \\, coarscoef_{i}=0 \\) while the rest are non-negative. Indeed, let \\( stability \\) be the largest of the (negative) numbers \\( -fullvalue_{i} / coarscoef_{i} \\), considering only \\( i \\)'s for which \\( coarscoef_{i}>0 \\), of which there must be at least one. But this gives a representation of \\( lackness \\) as a convex combination of fewer than \\( q \\) elements of \\( emptiness \\), which is impossible because of our choice of \\( q \\). This contradiction proves the theorem.\n\nReturning to the problem, let \\( concavity \\) be the convex hull of \\( vacuumbase_{0} \\). If \\( universe \\) is any subset of \\( concavity \\), then \\( universe \\subseteq diverging(universe) \\subseteq concavity \\). (The first inclusion requires that degenerate segments be allowed; \\( nullpoint \\) is on the \"segment\" from \\( nullpoint \\) to \\( nullpoint \\).) It follows that\n\\[\nvacuumbase_{0} \\subseteq vacuityseq_{1} \\subseteq vacuityseq_{2} \\subseteq vacuityseq_{3} \\subseteq \\cdots \\subseteq concavity\n\\]\n\nSuppose \\( lackness \\in concavity \\). By the theorem we can write\n\\[\nlackness=fullvalue_{1} nonpoint_{1}+fullvalue_{2} nonpoint_{2}+fullvalue_{3} nonpoint_{3}+fullvalue_{4} nonpoint_{4},\n\\]\nwhere \\( nonpoint_{i} \\in vacuumbase_{0},\\  \\Sigma fullvalue_{i}=1 \\), and \\( fullvalue_{i}>0 \\). (If perchance \\( lackness \\) can be represented as a convex combination of fewer than four points of \\( vacuumbase_{0} \\), we can allow several of the \\( nonpoint_{i} \\)'s to be the same and thus arrange that all the coefficients are positive.) Put\n\\[\n\\begin{array}{l}\nlowpoint=\\dfrac{fullvalue_{1}}{fullvalue_{1}+fullvalue_{2}}\\, nonpoint_{1}+\\dfrac{fullvalue_{2}}{fullvalue_{1}+fullvalue_{2}}\\, nonpoint_{2} \\\\ [6pt]\nendpoint=\\dfrac{fullvalue_{3}}{fullvalue_{3}+fullvalue_{4}}\\, nonpoint_{3}+\\dfrac{fullvalue_{4}}{fullvalue_{3}+fullvalue_{4}}\\, nonpoint_{4}\n\\end{array}\n\\]\n\nThen\n\\[\n\\begin{array}{c}\nlowpoint \\in diverging\\left(vacuumbase_{0}\\right)=vacuityseq_{1}, \\quad endpoint \\in vacuityseq_{1}, \\quad \\text { and } \\\\[4pt]\nlackness=\\left(fullvalue_{1}+fullvalue_{2}\\right) lowpoint+\\left(fullvalue_{3}+fullvalue_{4}\\right) endpoint \\in diverging\\left(vacuityseq_{1}\\right)=vacuityseq_{2}\n\\end{array}\n\\]\n\nThis proves that \\( concavity \\subseteq vacuityseq_{2} \\). Then from (3) it follows that\n\\[\nvacuityseq_{2}=vacuityseq_{3}=\\cdots=concavity\n\\]\n>>>"
    },
    "garbled_string": {
      "map": {
        "A": "fxqlmhnc",
        "S": "jgkvpqrs",
        "A_n": "dlxwzvbt",
        "a_i": "sqnbmxle",
        "p": "rbthcquo",
        "\\lambda_i": "kdlvhgna",
        "b_j": "rczfjqew",
        "c": "mghrxslo",
        "K": "zhpmrwqa",
        "q": "vndkjsbf",
        "\\mu_i": "ljtcrxha",
        "\\sigma": "wqmrtvyz",
        "r": "tclwmsqa",
        "x": "ghrnvpta",
        "X": "lqzdmyva",
        "E": "kxpqslmz",
        "n": "dfjhrcme",
        "A_0": "xnglqdva"
      },
      "question": "6. Let \\( kxpqslmz \\) be a Euclidean space of at most three dimensions. If \\( fxqlmhnc \\) is a nonempty subset of \\( kxpqslmz \\), define \\( jgkvpqrs(fxqlmhnc) \\) to be the set of all points that lie on closed segments joining pairs of points of \\( fxqlmhnc \\). For a given nonempty set \\( xnglqdva \\), define \\( dlxwzvbt \\equiv jgkvpqrs\\left(fxqlmhnc_{dfjhrcme-1}\\right) \\) for \\( dfjhrcme=1,2, \\ldots \\). Prove that \\( fxqlmhnc_{2}=fxqlmhnc_{3}=\\cdots \\). (A one-point set should be considered to be a special case of a closed segment.)",
      "solution": "Solution. We consider \\( kxpqslmz \\) as a vector space as usual.\nIf \\( sqnbmxle_{1}, sqnbmxle_{2}, \\ldots, sqnbmxle_{dfjhrcme} \\) are in \\( kxpqslmz \\), then any point (vector) \\( rbthcquo \\) which can be written in the form\n\\[\nrbthcquo=\\sum_{i=1}^{dfjhrcme} kdlvhgna sqnbmxle_{i}\n\\]\nwhere \\( kdlvhgna \\geq 0 \\) and \\( \\Sigma kdlvhgna=1 \\), is called a convex combination of the \\( sqnbmxle \\) 's.\nConvex combination is transitive; that is, if \\( rczfjqew_{1}, rczfjqew_{2}, \\ldots, rczfjqew_{j} \\) are each convex combinations of \\( sqnbmxle_{1}, sqnbmxle_{2}, \\ldots, sqnbmxle_{dfjhrcme} \\), and \\( mghrxslo \\) is a convex combination of \\( rczfjqew_{1}, rczfjqew_{2}, \\ldots, rczfjqew_{j} \\), then \\( mghrxslo \\) is a convex combination of \\( sqnbmxle_{1}, sqnbmxle_{2}, \\ldots, sqnbmxle_{dfjhrcme} \\).\nIt follows that, if \\( fxqlmhnc \\) is a given set in \\( kxpqslmz \\), the set \\( zhpmrwqa \\) of all convex combinations of elements of \\( fxqlmhnc \\) is a convex set; indeed, it is the smallest convex set containing \\( fxqlmhnc . zhpmrwqa \\) is called the convex hull of \\( fxqlmhnc \\).\n\nThe essence of the problem lies in the following theorem.\nTheorem. Suppose \\( kxpqslmz \\) has dimension \\( dfjhrcme \\), and \\( fxqlmhnc \\) is a subset of kxpqslmz. Then every point in the convex hull \\( zhpmrwqa \\) of \\( fxqlmhnc \\) can be written as a convex combination of at most \\( dfjhrcme+1 \\) points of \\( fxqlmhnc \\).\n\nProof. Suppose \\( rbthcquo \\in zhpmrwqa \\), but \\( rbthcquo \\) cannot be written as a convex combination of fewer than \\( dfjhrcme+2 \\) points of \\( fxqlmhnc \\). Since \\( rbthcquo \\in zhpmrwqa \\), we can write\n\\[\nrbthcquo=\\sum_{i=1}^{vndkjsbf} kdlvhgna sqnbmxle_{i}\n\\]\nwhere \\( sqnbmxle_{1} \\in fxqlmhnc . kdlvhgna \\geq 0 \\), and \\( \\Sigma kdlvhgna=1 \\). Of all such representations we choose one with \\( vndkjsbf \\) as small as possible. Then \\( vndkjsbf>dfjhrcme+1 \\).\n\nThere exist numbers \\( ljtcrxha_{1}, ljtcrxha_{2}, \\ldots, ljtcrxha_{vndkjsbf} \\), not all zero, such that\n\\[\n\\sum_{i \\pm 1}^{vndkjsbf} ljtcrxha=0\n\\]\n\\[\n\\sum_{i=1}^{4} ljtcrxha sqnbmxle_{i}=0,\n\\]\nsince (2) can be regarded as a system of \\( dfjhrcme+1 \\) linear homogeneous equations in more than \\( dfjhrcme+1 \\) unknowns \\( ljtcrxha \\).\n\nFrom (1) and (2) we have\n\\[\nrbthcquo=\\sum_{i=1}^{4}\\left(kdlvhgna+wqmrtvyz ljtcrxha\\right) sqnbmxle_{i}\n\\]\nfor any \\( wqmrtvyz \\in \\mathbf{R} \\). Here the coefficient sum is always 1. We can choose \\( wqmrtvyz \\) so that one of the new coefficients \\( kdlvhgna+wqmrtvyz ljtcrxha=0 \\) while the rest are non-negative. Indeed, let \\( wqmrtvyz \\) be the largest of the (negative) numbers \\( -kdlvhgna / ljtcrxha \\), considering only indices for which \\( ljtcrxha>0 \\), of which there must be at least one. But this gives a representation of \\( rbthcquo \\) as a convex combination of fewer than \\( vndkjsbf \\) elements of \\( fxqlmhnc \\), which is impossible because of our choice of \\( vndkjsbf \\). This contradiction proves the theorem.\n\nReturning to the problem, let \\( zhpmrwqa \\) be the convex hull of \\( xnglqdva \\). If \\( lqzdmyva \\) is any subset of \\( zhpmrwqa \\), then \\( lqzdmyva \\subseteq jgkvpqrs(lqzdmyva) \\subseteq zhpmrwqa \\). (The first inclusion requires that degenerate segments be allowed; \\( ghrnvpta \\) is on the \"segment\" from \\( ghrnvpta \\) to \\( ghrnvpta \\).) It follows that\n\\[\nxnglqdva \\subseteq fxqlmhnc_{1} \\subseteq fxqlmhnc_{2} \\subseteq fxqlmhnc_{3} \\subseteq \\cdots \\subseteq zhpmrwqa\n\\]\n\nSuppose \\( rbthcquo \\in zhpmrwqa \\). By the theorem we can write\n\\[\nrbthcquo=\\lambda_{1} sqnbmxle_{1}+\\lambda_{2} sqnbmxle_{2}+\\lambda_{3} sqnbmxle_{3}+\\lambda_{4} sqnbmxle_{4},\n\\]\nwhere \\( sqnbmxle_{i} \\in xnglqdva, \\Sigma \\lambda_{i}=1 \\), and \\( \\lambda_{i}>0 \\). (If perchance \\( rbthcquo \\) can be represented as a convex combination of fewer than four points of \\( xnglqdva \\), we can allow several of the \\( sqnbmxle_{i} \\) 's to be the same and thus arrange that all the coefficients are positive.) Put\n\\[\n\\begin{array}{l}\nvndkjsbf=\\frac{\\lambda_{1}}{\\lambda_{1}+\\lambda_{2}} sqnbmxle_{1}+\\frac{\\lambda_{2}}{\\lambda_{1}+\\lambda_{2}} sqnbmxle_{2} \\\\\ntclwmsqa=\\frac{\\lambda_{3}}{\\lambda_{3}+\\lambda_{4}} sqnbmxle_{3}+\\frac{\\lambda_{4}}{\\lambda_{3}+\\lambda_{4}} sqnbmxle_{4}\n\\end{array}\n\\]\n\nThen\n\\[\n\\begin{array}{c}\nvndkjsbf \\in jgkvpqrs\\left(xnglqdva\\right)=fxqlmhnc_{1}, \\quad tclwmsqa \\in fxqlmhnc_{1}, \\quad \\text { and } \\\\\nrbthcquo=\\left(\\lambda_{1}+\\lambda_{2}\\right) vndkjsbf+\\left(\\lambda_{3}+\\lambda_{4}\\right) tclwmsqa \\in jgkvpqrs\\left(fxqlmhnc_{1}\\right)=fxqlmhnc_{2}\n\\end{array}\n\\]\n\nThis proves that \\( zhpmrwqa \\subseteq fxqlmhnc_{2} \\). Then from (3) it follows that\n\\[\nfxqlmhnc_{2}=fxqlmhnc_{3}=\\cdots=zhpmrwqa\n\\]"
    },
    "kernel_variant": {
      "question": "Let $E$ be a {\\it real} Euclidean space of finite dimension $d\\ge 1$.  \nFor every non–empty set $X\\subset E$ put  \n\\[\nL(X)=\\bigl\\{(1-t)x+ty:\\;x,y\\in X,\\;0\\le t\\le 1\\bigr\\},\n\\]\nand start the iterative sequence  \n\\[\nB_{1}\\subset E\\ \\text{(non–empty and arbitrary)},\\qquad   \nB_{n}=L(B_{n-1})\\qquad(n\\ge 2).\n\\]\n\n1.\\;Prove that there exists an integer $k(d)\\ (\\!$depending only on the dimension $d)$ such that  \n   \\[\n     B_{k(d)+1}=B_{k(d)+2}=B_{k(d)+3}=\\dots \\qquad\\text{for \\emph{every} initial set }B_{1}.\n   \\]\n\n2.\\;Show that the \\emph{sharp} value of this stabilising index is  \n   \\[\n     k(d)=\\bigl\\lceil\\log_{2}(d+1)\\bigr\\rceil .\n   \\]\n\n   Equivalently,\n\n   • for all non–empty $B_{1}\\subset E$ one always has $B_{k(d)+1}= \\operatorname{conv}(B_{1})$;  \n\n   • for every $d$ there is a set $A\\subset E$ with  \n     \\[\n       B_{k(d)}(A):=L^{\\,k(d)-1}(A)\\subsetneq\\operatorname{conv}(A),\n     \\]\n     so that no earlier iterate is already the convex hull.\n\n   (Here $\\operatorname{conv}(A)$ is the convex hull of $A$ and $L^{\\,m}$ denotes the $m$-fold iterate of $L$.)",
      "solution": "Throughout we abbreviate  \n\\[\nk:=k(d):=\\bigl\\lceil\\log_{2}(d+1)\\bigr\\rceil .\n\\]\n\n--------------------------------------------------------------------\nStep 1.\\;A counting lemma (Carathéodory $+$ a refined induction).\n\nCarathéodory’s theorem states that every point of $\\operatorname{conv}(B_{1})$ can be written as a convex combination of at most $d+1$ points of $B_{1}$.\n\nLemma.\\;If a point $p$ can be expressed as a convex combination of at most $2^{m}$ points of $B_{1}$, then $p\\in B_{m+1}$.\n\nProof by induction on $m\\ge 0$.\n\n•\\;$m=0$.\\;The hypothesis says that $p$ is one point of $B_{1}$, hence  \n$p\\in B_{1}\\subset B_{2}$.\n\n•\\;Inductive step.\\;Assume the lemma true for $m-1$; suppose  \n\\[\np=\\sum_{i=1}^{s}\\lambda_{i}a_{i},\\qquad 1\\le s\\le 2^{m},\\;\n\\lambda_{i}\\ge 0,\\;\\sum_{i}\\lambda_{i}=1,\n\\]\nis such a representation.\n\nIf in fact $s\\le 2^{m-1}$, the induction hypothesis already gives  \n$p\\in B_{m}$ and hence trivially $p\\in B_{m+1}$.\n\nOtherwise $s>2^{m-1}$.\\;Split the index set into two (not necessarily equal) parts of size  \n$\\le 2^{m-1}$:\n\\[\nI_{1}=\\{1,\\dots ,2^{m-1}\\},\\qquad I_{2}=\\{2^{m-1}+1,\\dots ,s\\}.\n\\]\nWrite  \n\\[\n\\alpha=\\sum_{i\\in I_{1}}\\lambda_{i},\\qquad\n\\beta =\\sum_{i\\in I_{2}}\\lambda_{i}=1-\\alpha .\n\\]\nBecause $s>2^{m-1}$, both $\\alpha$ and $\\beta$ are \\emph{strictly positive}.  \nDefine\n\\[\nq=\\frac1{\\alpha}\\sum_{i\\in I_{1}}\\lambda_{i}a_{i},\\qquad\nr=\\frac1{\\beta }\\sum_{i\\in I_{2}}\\lambda_{i}a_{i}.\n\\]\nEach of $q,r$ is a convex combination of at most $2^{m-1}$ points of $B_{1}$, so by the induction hypothesis $q,r\\in B_{m}$. Finally\n\\[\np=(1-\\beta)q+\\beta r\\in L(B_{m})=B_{m+1},\n\\]\ncompleting the induction.\n\n--------------------------------------------------------------------\nStep 2.\\;Upper bound: $B_{k+1}=\\operatorname{conv}(B_{1})$.\n\nBecause $d+1\\le 2^{k}$ by definition of $k$, Carathéodory gives that every  \n$p\\in\\operatorname{conv}(B_{1})$ is a convex combination of at most $2^{k}$ points of $B_{1}$.  \nApplying the lemma with $m=k$ yields  \n\\[\n\\operatorname{conv}(B_{1})\\subset B_{k+1}.\n\\]\nConversely $B_{n}\\subset\\operatorname{conv}(B_{1})$ for every $n$ (the operator $L$ never leaves the convex hull). Therefore\n\\[\nB_{1}\\subset B_{2}\\subset\\dots\\subset B_{k+1}\\subset\\operatorname{conv}(B_{1})\\subset B_{k+1},\n\\]\nso $B_{k+1}=\\operatorname{conv}(B_{1})$. Since $L$ fixes convex sets,\n\\[\nB_{k+1}=B_{k+2}=B_{k+3}=\\dots ,\n\\]\nand we have proved $k(d)\\le k$.\n\n--------------------------------------------------------------------\nStep 3.\\;Lower bound and sharpness.\n\nFix $d$ and let $A=\\{v_{0},\\dots ,v_{d}\\}$ be the vertices of a $d$-simplex in $E$; the vectors $v_{0},\\dots ,v_{d}$ are affinely independent.  \nDenote its barycentre by  \n\\[\nc=\\frac1{d+1}\\sum_{i=0}^{d}v_{i}.\n\\]\n\nClaim 1.\\;$c$ cannot be written as a convex combination of fewer than $d+1$ of the vertices.  \nIndeed, in the representation\n\\[\nc=\\sum_{i=0}^{d}\\tfrac1{d+1}v_{i}\n\\]\nall coefficients are \\emph{strictly positive}.  \nIf a convex combination omitted one vertex, the remaining vertices would lie in a proper face of the simplex, so their convex hull would also lie in that face, contradicting that $c$ is an interior point.\n\nClaim 2.\\;Every point of $B_{m}(A)$ is a convex combination of at most $2^{m-1}$ vertices of the simplex.\n\nProof by induction on $m$.  \n$\\;m=1$ is obvious since $B_{1}=A$.  \nInductively, a point of $B_{m}$ is a convex combination of two points of $B_{m-1}$; by the inductive hypothesis each of those uses no more than $2^{m-2}$ vertices, so altogether at most $2\\cdot 2^{m-2}=2^{m-1}$ vertices occur.\n\nNow put $m=k$. Because $k-1<\\log_{2}(d+1)$ we have\n$d+1>2^{k-1}$.  \nBy Claim 2, every point of $B_{k}(A)$ is a convex combination of at most $2^{k-1}$ vertices, so by Claim 1 the barycentre $c\\notin B_{k}(A)$. Hence\n\\[\nB_{k}(A)\\subsetneq\\operatorname{conv}(A),\n\\]\nand therefore the sequence cannot stabilise before step $k+1$, showing $k(d)\\ge k$.\n\n--------------------------------------------------------------------\nStep 4.\\;Conclusion.\n\nSteps 2 and 3 give the exact value\n\\[\nk(d)=\\bigl\\lceil\\log_{2}(d+1)\\bigr\\rceil ,\n\\qquad\nB_{k(d)+1}=\\operatorname{conv}(B_{1}),\n\\qquad\nB_{k(d)+1}=B_{k(d)+2}=B_{k(d)+3}=\\dots ,\n\\]\nand the bound is optimal.\n\n--------------------------------------------------------------------\n(For comparison with low dimensions one recovers the classical facts  \n$k(1)=1$ (stabilisation after one step), $k(2)=2$, $k(3)=2$, $k(4)=3$, and so on.)",
      "metadata": {
        "replaced_from": "harder_variant",
        "replacement_date": "2025-07-14T19:09:31.545955",
        "was_fixed": false,
        "difficulty_analysis": "Compared to the original three–dimensional problem this variant is markedly harder:\n\n• Higher dimension: the statement now covers \\emph{all} finite dimensions and the\nstabilising index is not fixed but must be discovered as a function of \\(d\\).\n\n• Additional tasks: one must both establish an upper bound (constructive) \\emph{and}\nprove optimality (existence of a worst-case set), rather than only showing that the\nsequence eventually stabilises.\n\n• Deeper theory: the proof merges Carathéodory’s theorem with a non-trivial\ncombinatorial argument on binary trees of convex combinations and demands an\nexplicit counting argument for minimality.\n\n• Sharpness example: the solver has to design an affinely independent configuration\nand use interior-point arguments inside a simplex, something absent from the\noriginal exercise.\n\n• Complexity of reasoning: one must keep track simultaneously of\n(i) the number of vertices involved in nested convex constructions,\n(ii) logarithmic bounds,\n(iii) topological convex-hull invariance,\nmaking the chain of inclusions and equalities significantly longer and more intricate.\n\nThese cumulative layers ensure that the enhanced variant is substantially more\nchallenging than both the original and the current kernel versions."
      }
    },
    "original_kernel_variant": {
      "question": "Let \\(E\\) be a real Euclidean space of finite dimension \\(d\\ge 1\\).  \nFor a non–empty set \\(X\\subset E\\) define  \n\\[\nL(X)=\\{(1-t)x+ty:\\ x,y\\in X,\\;0\\le t\\le 1\\},\n\\]\nand start the sequence  \n\\[\nB_{1}\\subset E\\ \\text{(non–empty and arbitrary)},\\qquad B_{n}=L(B_{n-1})\\quad(n\\ge 2).\n\\]\n\n1.  Show that there exists an integer \\(k(d)\\) depending only on the dimension \\(d\\) such that  \n   \\[\n     B_{k(d)+1}=B_{k(d)+2}=B_{k(d)+3}=\\dots\\qquad\\text{for \\emph{every} initial set }B_{1}.\n   \\]\n\n2.  Prove that the sharp value of this stabilising index is  \n   \\[\n     k(d)=\\Bigl\\lceil\\log_{2}(d+1)\\Bigr\\rceil .\n   \\]\n\n   In other words,\n   • for every \\(B_{1}\\subset E\\) we always have \\(B_{k(d)+1}= \\operatorname{conv}(B_{1})\\);  \n   • for each \\(d\\) there exists a set \\(A\\subset E\\) for which \\(B_{k(d)}(A):=L^{k(d)-1}(A)\\) is a {\\it proper} subset of \\(\\operatorname{conv}(A)\\).\n\n   (Here \\(\\operatorname{conv}(A)\\) denotes the convex hull of \\(A\\) and \\(L^{m}\\) the \\(m\\)-fold iterate of \\(L\\).)",
      "solution": "Throughout we abbreviate \\(k:=k(d):=\\lceil\\log_{2}(d+1)\\rceil\\).\n\n--------------------------------------------------------------------\nStep 1.  (Carathéodory + a combinatorial observation)\n\nCarathéodory’s theorem:\nEvery point of \\(\\operatorname{conv}(B_{1})\\) can be written as a convex combination of at most \\(d+1\\) points of \\(B_{1}\\).\n\nCombinatorial observation:\nSuppose a point \\(p\\) is a convex combination of at most \\(2^{m}\\) points of \\(B_{1}\\).  \nThen \\(p\\in B_{m+1}\\).  \n\nProof by induction on \\(m\\).  \n• \\(m=0\\) is clear: “at most \\(1\\)” point means \\(p\\in B_{1}\\subset B_{2}\\).  \n• Inductive step: write\n\\[\np=\\sum_{i=1}^{2^{m}}\\lambda_i a_i,\\qquad\\lambda_i\\ge0,\\ \\sum\\lambda_i=1.\n\\]\nSplit the index set into the first \\(2^{m-1}\\) and the last \\(2^{m-1}\\) terms:\n\\[\nq=\\frac1{\\alpha}\\sum_{i=1}^{2^{m-1}}\\lambda_i a_i,\\qquad\nr=\\frac1{\\beta }\\sum_{i=2^{m-1}+1}^{2^{m}}\\lambda_i a_i,\n\\]\nwith \\(\\alpha+\\beta=1\\), \\(\\alpha ,\\beta\\ge0\\).  \nBy the inductive hypothesis \\(q,r\\in B_{m}\\) (each uses at most \\(2^{m-1}\\) points), hence\n\\(p=(1-\\beta)q+\\beta r\\in L(B_{m})=B_{m+1}\\).\n\n--------------------------------------------------------------------\nStep 2.  (Upper bound: \\(B_{k+1}\\) already equals the convex hull)\n\nBecause \\(d+1\\le 2^{k}\\) by the definition of \\(k\\), Carathéodory says that every\n\\(p\\in\\operatorname{conv}(B_{1})\\) is a convex combination of at most \\(2^{k}\\) points of \\(B_{1}\\).\nApplying the observation of Step 1 with \\(m=k\\) we obtain  \n\\[\n\\operatorname{conv}(B_{1})\\subset B_{k+1}.\n\\]\nOn the other hand \\(B_{n}\\subset\\operatorname{conv}(B_{1})\\) for every \\(n\\)\n(because the operator \\(L\\) never leaves the convex hull).\nHence\n\\[\nB_{1}\\subset B_{2}\\subset\\dots\\subset B_{k+1}\\subset\\operatorname{conv}(B_{1})\n\\subset B_{k+1},\n\\]\nso \\(B_{k+1}=\\operatorname{conv}(B_{1})\\).\nApplying \\(L\\) once more does nothing (a convex set is fixed by \\(L\\)),\nand therefore\n\\[\nB_{k+1}=B_{k+2}=B_{k+3}=\\dots\n\\]\nfor every starting set, proving the existence of \\(k(d)\\) and the upper bound \\(k(d)\\le k\\).\n\n--------------------------------------------------------------------\nStep 3.  (Lower bound and sharpness)\n\nFix \\(d\\) and take \\(A=\\{v_{0},\\dots ,v_{d}\\}\\) to be the vertices of a\n\\(d\\)-simplex in \\(E\\) (hence affinely independent).\nLet \\(c=\\frac1{d+1}\\sum_{i=0}^{d}v_{i}\\) be its barycentre.\n\nClaim 1.  \\(c\\) cannot be expressed as a convex combination of fewer than \\(d+1\\)\nvertices of the simplex.  \n(Indeed, a combination using at most \\(d\\) vertices would place \\(c\\) in one of the\nfaces, contradicting that \\(c\\) is an interior point.)\n\nClaim 2.  Every point in \\(B_{m}(A)\\) is a convex combination of at most \\(2^{m-1}\\)\nvertices of the simplex.  \nProof again by induction:  \n• \\(m=1\\): \\(B_{1}=A\\).  \n• Inductive step: points of \\(B_{m}\\) are convex combinations of two points of\n\\(B_{m-1}\\), hence of at most \\(2\\cdot2^{m-2}=2^{m-1}\\) vertices.\n\nTake \\(m=k\\).  Since \\(d+1>2^{k-1}\\) (because \\(k-1<\\log_2(d+1)\\)),\nClaim 2 shows that \\(B_{k}(A)\\) contains only convex combinations of at most\n\\(2^{k-1}\\) vertices, so by Claim 1 the barycentre \\(c\\notin B_{k}(A)\\).\nTherefore \\(B_{k}(A)\\subsetneq\\operatorname{conv}(A)\\).\n\nConsequently the sequence does not stabilise before step \\(k+1\\),\nand we have shown \\(k(d)\\ge k\\).\n\n--------------------------------------------------------------------\nStep 4.  (Conclusion)\n\nCombining Steps 2 and 3 we have\n\\[\nk(d)=\\Bigl\\lceil\\log_{2}(d+1)\\Bigr\\rceil ,\n\\qquad\nB_{k(d)+1}=\\operatorname{conv}(B_{1}),\n\\qquad\nB_{k(d)+1}=B_{k(d)+2}=B_{k(d)+3}=\\dots ,\n\\]\nand the bound is optimal.\n\n--------------------------------------------------------------------\n(For small dimensions one recovers the classical facts:\n\\(k(1)=1\\;(B_{2}\\) stabilises), \\(k(2)=2\\;(B_{3}\\) stabilises),\n\\(k(3)=2\\) (the original problem), \\(k(4)=3\\), etc.)",
      "metadata": {
        "replaced_from": "harder_variant",
        "replacement_date": "2025-07-14T01:37:45.452202",
        "was_fixed": false,
        "difficulty_analysis": "Compared to the original three–dimensional problem this variant is markedly harder:\n\n• Higher dimension: the statement now covers \\emph{all} finite dimensions and the\nstabilising index is not fixed but must be discovered as a function of \\(d\\).\n\n• Additional tasks: one must both establish an upper bound (constructive) \\emph{and}\nprove optimality (existence of a worst-case set), rather than only showing that the\nsequence eventually stabilises.\n\n• Deeper theory: the proof merges Carathéodory’s theorem with a non-trivial\ncombinatorial argument on binary trees of convex combinations and demands an\nexplicit counting argument for minimality.\n\n• Sharpness example: the solver has to design an affinely independent configuration\nand use interior-point arguments inside a simplex, something absent from the\noriginal exercise.\n\n• Complexity of reasoning: one must keep track simultaneously of\n(i) the number of vertices involved in nested convex constructions,\n(ii) logarithmic bounds,\n(iii) topological convex-hull invariance,\nmaking the chain of inclusions and equalities significantly longer and more intricate.\n\nThese cumulative layers ensure that the enhanced variant is substantially more\nchallenging than both the original and the current kernel versions."
      }
    }
  },
  "checked": true,
  "problem_type": "proof"
}