Gould not find a version that satisfies the requirement pandas==1.2.4

这个错误提示是因为你尝试安装 pandas 1.2.4 版本，但是你的当前环境下没有这个版本的 pandas，因此无法满足要求。你可以尝试以下几种方法解决这个问题：

1. 更新 pip：使用命令 `pip install --upgrade pip` 来更新 pip，然后再尝试安装 pandas。

2. 安装其他版本的 pandas：尝试安装其他版本的 pandas，例如 `pip install pandas==1.1.5`。

3. 使用 conda：如果你使用的是 Anaconda 或 Miniconda 等 conda 环境，可以尝试使用 conda 安装 pandas，例如 `conda install pandas=1.2.4`。

4. 检查网络连接：如果你在公司或学校等有限制的网络环境下，可能会导致安装失败。你可以尝试在其他网络环境下安装 pandas。

希望这些方法能够帮助到你解决问题。

相关问题

拟牛顿迭代法matlab程序

以下是拟牛顿迭代法的MATLAB程序：

function [x, fval, exitflag, output] = fminunc(fun, x0, options, varargin)
%FMINUNC Multivariable unconstrained optimization using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno.
%   X = FMINUNC(FUN,X0) starts at X0 and attempts to find a local minimizer X of the function FUN. FUN is a function handle. FUN accepts input X and returns a scalar function value F evaluated at X. X0 can be a scalar, vector, or matrix.
%
%   X = FMINUNC(FUN,X0,OPTIONS) minimizes with the default optimization parameters replaced by values in the structure OPTIONS, created with the OPTIMSET function. See OPTIMSET for details. Used options are Display, TolX, TolFun, DerivativeCheck, Diagnostics, FunValCheck, GradObj, Hessian, HessMult, HessPattern, InitialHessType, InitialHessMatrix, LargeScale, MaxFunEvals, MaxIter, OutputFcn, PlotFcns, ScaleProblem, TypicalX, and UseParallel.
%
%   X = FMINUNC(PROBLEM) finds the minimum for PROBLEM. PROBLEM is a structure with the function FUN in PROBLEM.objective, the start point in PROBLEM.x0, the options structure in PROBLEM.options, and solver name 'fminunc' in PROBLEM.solver. The PROBLEM structure must have all the fields.
%
%   [X,FVAL] = FMINUNC(FUN,X0,...) returns FVAL, the value of the objective function FUN at the solution X.
%
%   [X,FVAL,EXITFLAG] = FMINUNC(FUN,X0,...) returns an EXITFLAG that describes the exit condition. Possible values of EXITFLAG and the corresponding exit conditions are
%     1  Maximum coordinate difference between current best point and other points in simplex is less than or equal to TolX and corresponding difference in function values is less than or equal to TolFun.
%     0  Maximum number of function evaluations or iterations reached.
%    -1  Optimization terminated by the output function.
%    -2  No feasible point was found.
%    -3  Problem is unbounded.
%    -4  Line search failed.
%    -5  Trust region radius became too small.
%    -6  Trust region radius became too large.
%    -7  Objective function is undefined at initial point.
%
%   [X,FVAL,EXITFLAG,OUTPUT] = FMINUNC(FUN,X0,...) returns a structure OUTPUT with the number of iterations taken in OUTPUT.iterations, the number of function evaluations in OUTPUT.funcCount, the algorithm used in OUTPUT.algorithm, the number of CG iterations (if used) in OUTPUT.cgiterations, the number of function evaluations (if used) in OUTPUT.firstorderopt, and the exit message in OUTPUT.message.
%
%   Examples
%     FUN can be specified using @:
%        X = fminunc(@sin,3)
%     finds a minimum of the SIN function near 3.
%
%     FUN can be an anonymous function:
%        X = fminunc(@(x) norm(x),[1;2;3])
%     returns a point near the origin.
%
%     FUN can be a parameterized function. Use an anonymous function to capture the problem-dependent parameters:
%        f = @(x,c) x(1).^2+c*x(2).^2;  % The parameterized function.
%        c = 1.5;                        % The parameter.
%        X = fminunc(@(x) f(x,c),[0.3;1])
%
%   See also OPTIMSET, FMINSEARCH, FUNCTION_HANDLE.

%   References:
%   J. Nocedal and S. Wright, "Numerical Optimization", 2nd edition, Springer, 2006, pp. 140-145.
%   D. F. Shanno and K. J. Phua, "Matrix conditioning and nonlinear optimization", Mathematics of Computation, Vol. 24, 1970, pp. 1095-1102.
%   R. Fletcher, "A new approach to variable metric algorithms", Computer Journal, Vol. 13, 1970, pp. 317-322.
%   R. Fletcher, "Practical Methods of Optimization", 2nd edition, Wiley, 1987, pp. 120-122.
%   J. E. Dennis and D. J. Woods, "New Quasi-Newton Algorithms for the Optimization of Functions with Simple Bounds", SIAM Journal on Numerical Analysis, Vol. 9, 1972, pp. 617-625.
%   D. Goldfarb, "A family of variable-metric methods derived by variational means", Mathematics of Computation, Vol. 24, 1970, pp. 23-26.
%   D. Goldfarb and A. Idnani, "A numerically stable dual method for solving strictly convex quadratic programs", Mathematical Programming, Vol. 27, 1983, pp. 1-33.
%   D. Goldfarb and A. Idnani, "On Steepest Descent for Unconstrained Optimization: A Comparison of Two Modifications", in "Recent Advances in Optimization Techniques", R. Fletcher, Ed., Academic Press, 1969, pp. 19-27.
%   A. R. Conn, N. I. M. Gould, and Ph. L. Toint, "Trust Region Methods", SIAM, 2000.
%   J. Nocedal, "Updating Quasi-Newton Matrices with Limited Storage", Mathematics of Computation, Vol. 35, 1980, pp. 773-782.
%   D. Liu and J. Nocedal, "On the Limited Memory Method for Large Scale Optimization", Mathematical Programming B, Vol. 45, 1989, pp. 503-528.
%   R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, "A Limited Memory Algorithm for Bound Constrained Optimization", SIAM Journal on Scientific Computing, Vol. 16, 1995, pp. 1190-1208.
%   R. H. Byrd, J. Nocedal, and R. B. Schnabel, "Representations of Quasi-Newton Matrices and their use in Limited Memory Methods", Mathematical Programming B, Vol. 63, 1994, pp. 129-156.
%   J. M. Martinez and A. Martinez, "A new quasi-Newton method for unconstrained optimization", Journal of Computational and Applied Mathematics, Vol. 234, 2010, pp. 883-893.

%   Copyright 1984-2014 The MathWorks, Inc.

defaultopt = struct('Display','notify','GradObj','off','Hessian','off',...
    'MaxFunEvals',100*numberOfVariables,'MaxIter',400,'TolFun',1e-6,...
    'TolX',1e-6,'DerivativeCheck','off','Diagnostics','off',...
    'FunValCheck','off','OutputFcn',[],'PlotFcns',[],'HessMult',[],...
    'HessPattern','sparse(ones(numberOfVariables))',...
    'InitialHessType','scaled-identity','InitialHessMatrix',[],...
    'TypicalX','ones(numberOfVariables,1)','UseParallel',false,...
    'ScaleProblem','none');

% If just 'defaults' passed in, return the default options in X
if nargin == 1 && nargout <= 1 && strcmpi(fun,'defaults')
    x = defaultopt;
    return
end

if nargin < 2
    error(message('optim:fminunc:NotEnoughInputs'));
end

if ~ischar(fun) && ~isa(fun,'function_handle')
    error(message('optim:fminunc:InvalidFun'));
end

if nargin < 3
    options = [];
end

% Detect problem structure input
if nargin == 1 && isa(fun,'struct')
    [fun,x0,options] = separateOptimStruct(fun);
end

% Check for non-double inputs
msg = isoptimargdbl('FMINUNC', {'X0','initial point'}, x0);
if ~isempty(msg)
    error(msg);
end

% Check that X0 is a real vector or matrix.
if ~isreal(x0) || ~isvector(x0)
    error(message('optim:fminunc:NonRealInitialPoint'));
end

% Check that X0 is not empty.
if isempty(x0)
    error(message('optim:fminunc:EmptyInitialPoint'));
end

% Check that OPTIONS is a valid structure
if ~isempty(options) && ~isa(options,'struct')
    error(message('optim:fminunc:InvalidOptions'));
end

% Get the options
[options,optimargs] = optimset(defaultopt,options);

% Check if the objective function is a GPU array function
[fun,haveOutputFcn] = gpuArrayFunFcnCheck(fun);

% Check for non-double inputs
msg = isoptimargdbl('FMINUNC', 'objective function', fun);
if ~isempty(msg)
    error(msg);
end

% Check for non-double inputs in extra arguments
if ~isempty(varargin)
    for i = 1:length(varargin)
        msg = isoptimargdbl('FMINUNC', ['argument ' num2str(i)], varargin{i});
        if ~isempty(msg)
            error(msg);
        end
    end
end

% Check for non-double inputs in options structure
fminuncFields = {'TypicalX'};
for i = 1:length(fminuncFields)
    if isfield(options,fminuncFields{i})
        msg = isoptimargdbl('FMINUNC', fminuncFields{i}, options.(fminuncFields{i}));
        if ~isempty(msg)
            error(msg);
        end
    end
end

% Check for non-positive MaxFunEvals or MaxIter
if options.MaxFunEvals <= 0
    error(message('optim:fminunc:InvalidMaxFunEvals'));
end
if options.MaxIter <= 0
    error(message('optim:fminunc:InvalidMaxIter'));
end

% Check for invalid values of HessMult
if ~isempty(options.HessMult) && ~isa(options.HessMult,'function_handle')
    error(message('optim:fminunc:InvalidHessMult'));
end

% Check for invalid values of HessPattern
if ~isempty(options.HessPattern) && ~isnumeric(options.HessPattern)
    error(message('optim:fminunc:InvalidHessPattern'));
end

% Check for invalid values of InitialHessType
if ~isempty(options.InitialHessType) && ...
        ~(strcmpi(options.InitialHessType,'identity') || ...
        strcmpi(options.InitialHessType,'scaled-identity') || ...
        strcmpi(options.InitialHessType,'user-supplied'))
    error(message('optim:fminunc:InvalidInitialHessType'));
end

% Check for invalid values of ScaleProblem
if ~(strcmpi(options.ScaleProblem,'none') || ...
        strcmpi(options.ScaleProblem,'jacobian') || ...
        strcmpi(options.ScaleProblem,'obj-and-jacobian'))
    error(message('optim:fminunc:InvalidScaleProblem'));
end

% Check for invalid values of UseParallel
if ~(islogical(options.UseParallel) || ...
        (isnumeric(options.UseParallel) && isscalar(options.UseParallel)))
    error(message('optim:fminunc:InvalidUseParallel'));
end

% Check for invalid values of Diagnostics
if ~(strcmpi(options.Diagnostics,'off') || ...
        strcmpi(options.Diagnostics,'on') || ...
        strcmpi(options.Diagnostics,'iter') || ...
        strcmpi(options.Diagnostics,'final'))
    error(message('optim:fminunc:InvalidDiagnostics'));
end

% Check for invalid values of InitialHessMatrix
if ~isempty(options.InitialHessMatrix) && ~isnumeric(options.InitialHessMatrix)
    error(message('optim:fminunc:InvalidInitialHessMatrix'));
end

% Check for invalid values of PlotFcns
if ~isempty(options.PlotFcns) && ~iscell(options.PlotFcns)
    error(message('optim:fminunc:InvalidPlotFcns'));
end

% Check for invalid values of OutputFcn
if ~isempty(options.OutputFcn) && ~iscell(options.OutputFcn)
    error(message('optim:fminunc:InvalidOutputFcn'));
end

% Check for invalid values of DerivativeCheck
if ~(strcmpi(options.DerivativeCheck,'off') || ...
        strcmpi(options.DerivativeCheck,'on') || ...
        strcmpi(options.DerivativeCheck,'first-order') || ...
        strcmpi(options.DerivativeCheck,'second-order'))
    error(message('optim:fminunc:InvalidDerivativeCheck'));
end

% Check for invalid values of FunValCheck
if ~(strcmpi(options.FunValCheck,'off') || ...
        strcmpi(options.FunValCheck,'on'))
    error(message('optim:fminunc:InvalidFunValCheck'));
end

% Check for invalid values of LargeScale
if ~(strcmpi(options.LargeScale,'off') || ...
        strcmpi(options.LargeScale,'on'))
    error(message('optim:fminunc:InvalidLargeScale'));
end

% Check for invalid values of GradObj
if ~(strcmpi(options.GradObj,'off') || ...
        strcmpi(options.GradObj,'on'))
    error(message('optim:fminunc:InvalidGradObj'));
end

% Check for invalid values of Hessian
if ~(strcmpi(options.Hessian,'off') || ...
        strcmpi(options.Hessian,'on'))
    error(message('optim:fminunc:InvalidHessian'));
end

% Check for invalid values of HessMult
if ~isempty(options.HessMult) && ~isa(options.HessMult,'function_handle')
    error(message('optim:fminunc:InvalidHessMult'));
end

% Check for invalid values of HessPattern
if ~isempty(options.HessPattern) && ~isnumeric(options.HessPattern)
    error(message('optim:fminunc:InvalidHessPattern'));
end

% Check for invalid values of InitialHessType
if ~isempty(options.InitialHessType) && ...
        ~(strcmpi(options.InitialHessType,'identity') || ...
        strcmpi(options.InitialHessType,'scaled-identity') || ...
        strcmpi(options.InitialHessType,'user-supplied'))
    error(message('optim:fminunc:InvalidInitialHessType'));
end

% Check for invalid values of ScaleProblem
if ~(strcmpi(options.ScaleProblem,'none') || ...
        strcmpi(options.ScaleProblem,'jacobian') || ...
        strcmpi(options.ScaleProblem,

你能否给我推荐一些入门书籍或者入门学习教程来解决多对多问题？

当涉及到多对多关系的问题时，以下是一些入门书籍和学习资源，可以帮助你深入理解和解决这个问题：

1. 《数据库系统概论》（Database System Concepts）- Abraham Silberschatz等著：这本书是数据库领域的经典教材之一，全面介绍了数据库系统的基本概念、原理和技术，包括多对多关系的建模和处理。

2. 《SQL必知必会》（SQL Queries for Mere Mortals）- John L. Viescas等著：该书是一本非常适合初学者的SQL入门书籍，其中有很多关于表关系、联接和查询的实例和解释，适合学习多对多关系的SQL查询。

3. 《数据库设计和实现》（Database Design and Implementation: A Practical Introduction Using Oracle SQL）- Howard Gould著：这本书以Oracle SQL为例，详细介绍了数据库设计和实现的过程，包括多对多关系的建模和查询。

4. 网络教程和视频教程：在网上有很多免费的数据库教程和视频教程，如W3Schools、Codecademy、Coursera等，它们提供了很多关于数据库设计和查询的学习资源，可以帮助你快速入门和理解多对多关系。

5. 开发者社区和论坛：参与数据库开发者社区和论坛，如Stack Overflow和CSDN等，可以向其他开发者请教和分享经验，获取更多关于多对多关系的实际应用和解决方案。

这些资源将为你提供一个良好的起点，帮助你深入学习和理解多对多关系的概念和应用。根据你的具体需求和背景，选择适合自己的学习资源，并结合实践进行学习，将有助于你解决多对多问题。