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科研方法论答案.html
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<!DOCTYPE html><html lang="zh-CN"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=2"><meta name="theme-color" content="#222"><meta name="generator" content="Hexo 6.0.0"><link rel="apple-touch-icon" sizes="180x180" href="/images/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="/images/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="/images/favicon-16x16.png"><link rel="mask-icon" href="/images/logo.svg" color="#222"><link rel="stylesheet" href="/css/main.css"><link rel="stylesheet" href="/lib/font-awesome/css/all.min.css"><script id="hexo-configurations">var NexT=window.NexT||{},CONFIG={hostname:"qingjiu.life",root:"/",scheme:"Gemini",version:"7.8.0",exturl:!1,sidebar:{position:"right",display:"post",padding:18,offset:12,onmobile:!1},copycode:{enable:!0,show_result:!0,style:"mac"},back2top:{enable:!0,sidebar:!0,scrollpercent:!0},bookmark:{enable:!1,color:"#222",save:"auto"},fancybox:!1,mediumzoom:!1,lazyload:!1,pangu:!1,comments:{style:"tabs",active:null,storage:!0,lazyload:!1,nav:null},algolia:{hits:{per_page:10},labels:{input_placeholder:"Search for Posts",hits_empty:"We didn't find any results for the search: ${query}",hits_stats:"${hits} results found in ${time} ms"}},localsearch:{enable:!1,trigger:"auto",top_n_per_article:1,unescape:!1,preload:!1},motion:{enable:!0,async:!1,transition:{post_block:"fadeIn",post_header:"slideDownIn",post_body:"slideDownIn",coll_header:"slideLeftIn",sidebar:"slideUpIn"}},path:"search.xml"}</script><meta name="description" content="1备注：正确与否不做保证，但尽力保证答案正确，仅供参考！！ 第3章 数值计算方法3.1 常用算法和计算机辅助软件3.1.1【简答题】 试用你熟悉的程序设计软件设计一个三角追赶法的函数，求解问题为L*Ux=b，要求输入为L、U和b，输出为x。 我的答案： 1、根据题意，所输入的L、U三角矩阵用向量a,p,q来表示。"><meta property="og:type" content="article"><meta property="og:title" content="科研方法论答案"><meta property="og:url" content="https://qingjiu.life/%E7%A7%91%E7%A0%94%E6%96%B9%E6%B3%95%E8%AE%BA%E7%AD%94%E6%A1%88.html"><meta property="og:site_name" content="青酒的代码馆"><meta property="og:description" content="1备注：正确与否不做保证，但尽力保证答案正确，仅供参考！！ 第3章 数值计算方法3.1 常用算法和计算机辅助软件3.1.1【简答题】 试用你熟悉的程序设计软件设计一个三角追赶法的函数，求解问题为L*Ux=b，要求输入为L、U和b，输出为x。 我的答案： 1、根据题意，所输入的L、U三角矩阵用向量a,p,q来表示。"><meta property="og:locale" content="zh_CN"><meta property="og:image" 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w38J7WS11a81C3vJrR5ohb6bc3WBggEtFGwXkfxEevvXPfsu/tA6X8QfB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%0ArSaKvO/2gPD3w71PS7Ofx5daTp/2Qs1ldz35sriAnG7ypFZXHQZCnnA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bqBmrZ3n7yPPTeOPxoqhbykyxLn+IZ/Oiq3JMC+uP9Kk/3z/OqrTZ+bNV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%0AK43DnBx6VPKVdHE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N9Ac+oFbVRz28F1C0NxDHNEwwySKGU/UGt/bRn/FV/NaP/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%0A9akm82z1xITJbrDMstqZFDDP7yMbxsY56HcOB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IpoDzPXGt38TXV9FFK/h2K4iTWfLb93JMucNtxyF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%0ARjHOeUvNW1DULx7y7vJ5bhlZTIznIU5yo9F5IwOOTXX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property="article:published_time" content="2022-03-09T08:00:00.000Z"><meta property="article:modified_time" content="2022-03-14T01:24:02.194Z"><meta property="article:author" content="青酒"><meta property="article:tag" content="基础理论"><meta name="twitter:card" content="summary"><meta name="twitter:image" 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class="post-header"><h1 class="post-title" itemprop="name headline">科研方法论答案</h1><div class="post-meta"><span class="post-meta-item"><span class="post-meta-item-icon"><i class="far fa-calendar"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2022-03-09 16:00:00" itemprop="dateCreated datePublished" datetime="2022-03-09T16:00:00+08:00">2022-03-09</time> </span><span class="post-meta-item"><span class="post-meta-item-icon"><i class="far fa-calendar-check"></i> </span><span class="post-meta-item-text">更新于</span> <time title="修改时间：2022-03-14 09:24:02" itemprop="dateModified" datetime="2022-03-14T09:24:02+08:00">2022-03-14</time> </span><span class="post-meta-item"><span class="post-meta-item-icon"><i class="far fa-folder"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/%E5%9F%BA%E7%A1%80%E7%90%86%E8%AE%BA/" itemprop="url" rel="index"><span itemprop="name">基础理论</span></a> </span></span><span class="post-meta-item" title="阅读次数" id="busuanzi_container_page_pv" style="display:none"><span class="post-meta-item-icon"><i class="fa fa-eye"></i> </span><span class="post-meta-item-text">阅读次数：</span> <span id="busuanzi_value_page_pv"></span></span></div></header><div class="post-body" itemprop="articleBody"><figure class="highlight"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">备注：正确与否不做保证，但尽力保证答案正确，仅供参考！！</span><br></pre></td></tr></table></figure><h3 id="第3章-数值计算方法"><a href="#第3章-数值计算方法" class="headerlink" title="第3章 数值计算方法"></a>第3章 数值计算方法</h3><h4 id="3-1-常用算法和计算机辅助软件"><a href="#3-1-常用算法和计算机辅助软件" class="headerlink" title="3.1 常用算法和计算机辅助软件"></a>3.1 常用算法和计算机辅助软件</h4><h5 id="3-1-1【简答题】"><a href="#3-1-1【简答题】" class="headerlink" title="3.1.1【简答题】"></a>3.1.1【简答题】</h5><blockquote><p>试用你熟悉的程序设计软件设计一个三角追赶法的函数，求解问题为L*Ux=b，要求输入为L、U和b，输出为x。</p></blockquote><p>我的答案：</p><p>1、根据题意，所输入的L、U三角矩阵用向量a,p,q来表示。<span id="more"></span>输入输出格式如下：</p><p><strong>输入</strong>：（以四阶矩阵为例）<br>$$<br>L=\begin{pmatrix}<br>p_{1} & 0 & 0 & 0 \<br>a_{2} & p_{2} & 0 & 0 \<br>0 & a_{3} & p_{3} & 0 \<br>0 & 0 & a_{4} & p_{4}<br>\end{pmatrix}<br>,U=\begin{pmatrix}<br>1 & q_{1} & 0 & 0 \<br>0 & 1 & q_{2} & 0 \<br>0 & 0 & 1 & q_{3} \<br>0 & 0 & 0 & 1<br>\end{pmatrix}<br>,b=(b_{1},b_{2},b_{3},b_{4})<br>$$<br><strong>输出</strong>：解向量x</p><p>2、实现语言：MATLAB</p><p>3、实现代码：</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">function</span> <span class="title">x</span>=<span class="title">ForwardBackward</span><span class="params">(a,p,q,b)</span></span></span><br><span class="line">n=<span class="built_in">length</span>(b);<span class="comment">%n为方程的维度</span></span><br><span class="line"><span class="comment">%步骤1：解下三角方程组Ly=b</span></span><br><span class="line">y(<span class="number">1</span>)=b(<span class="number">1</span>)/p(<span class="number">1</span>);</span><br><span class="line"><span class="keyword">for</span> <span class="built_in">i</span>=<span class="number">2</span>:n</span><br><span class="line"> y(<span class="built_in">i</span>)=(b(<span class="built_in">i</span>)-a(<span class="built_in">i</span>)*y(<span class="built_in">i</span><span class="number">-1</span>))/p(<span class="built_in">i</span>);</span><br><span class="line"><span class="keyword">end</span></span><br><span class="line"> </span><br><span class="line"><span class="comment">%步骤2：解上三角方程组Ux=y</span></span><br><span class="line">x(n)=y(n);</span><br><span class="line"><span class="keyword">for</span> <span class="built_in">i</span>=n<span class="number">-1</span>:<span class="number">-1</span>:<span class="number">1</span></span><br><span class="line"> x(<span class="built_in">i</span>)=y(<span class="built_in">i</span>)-q(<span class="built_in">i</span>)*x(<span class="built_in">i</span>+<span class="number">1</span>);</span><br><span class="line"><span class="keyword">end</span></span><br><span class="line">x=x';<span class="comment">%转置向量</span></span><br><span class="line"><span class="keyword">end</span></span><br></pre></td></tr></table></figure><h5 id="3-1-2【简答题】"><a href="#3-1-2【简答题】" class="headerlink" title="3.1.2【简答题】"></a>3.1.2【简答题】</h5><blockquote><p>试用你熟悉的程序设计软件，设计一个二分法求非线性方程根的程序。</p></blockquote><p>我的答案：</p><p>1、二分法求解思路：</p><p>根据零点定理，若区间中值的函数值小于0，则根在区间右半部分；大于0则在区间左半部分。反复迭代求解直到满足所给误差为止。</p><p>2、实现语言：MATLAB</p><p>3、实现代码：</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">%可自定一个非线性方程，以y=x^3-x^2+2x为例</span></span><br><span class="line"><span class="function"><span class="keyword">function</span> <span class="title">y</span> = <span class="title">myFunc</span><span class="params">(inputArg1)</span></span></span><br><span class="line"> y=inputArg1^<span class="number">3</span>-inputArg1^<span class="number">2</span>+<span class="number">2</span>*inputArg1;</span><br><span class="line"><span class="keyword">end</span></span><br><span class="line"></span><br><span class="line"><span class="comment">%二分法求非线性方程根</span></span><br><span class="line"><span class="comment">%输入：</span></span><br><span class="line"><span class="comment">% low:区间下界</span></span><br><span class="line"><span class="comment">% upper:区间上界</span></span><br><span class="line"><span class="comment">% TOL:误差容限</span></span><br><span class="line"><span class="function"><span class="keyword">function</span> <span class="title">BinarySolution</span><span class="params">(low,upper,TOL)</span></span></span><br><span class="line"> a=low;b=upper;</span><br><span class="line"> m=<span class="built_in">ceil</span>(<span class="built_in">log</span>((b-a)/TOL)/<span class="built_in">log</span>(<span class="number">2</span>)); </span><br><span class="line"> <span class="comment">% m为最大迭代次数与区间长度和误差容限有关</span></span><br><span class="line"> <span class="keyword">for</span> k=<span class="number">1</span>:m</span><br><span class="line"> p=(a+b)/<span class="number">2</span>;</span><br><span class="line"> <span class="keyword">if</span> myFunc(p)*myFunc(b)<<span class="number">0</span></span><br><span class="line"> a=p;</span><br><span class="line"> <span class="keyword">else</span></span><br><span class="line"> b=p;</span><br><span class="line"> <span class="keyword">end</span></span><br><span class="line"> <span class="keyword">end</span></span><br><span class="line"> <span class="built_in">disp</span>([<span class="string">'经过二分法求得的根为：x='</span>,num2str((a+b)/<span class="number">2</span>,<span class="string">'%.6f'</span>)])</span><br><span class="line"> <span class="built_in">disp</span>([<span class="string">'共经过'</span>,num2str(k),<span class="string">'次计算'</span>])</span><br><span class="line"><span class="keyword">end</span></span><br></pre></td></tr></table></figure><p>参考链接：<a target="_blank" rel="noopener" href="https://blog.csdn.net/xiaoye_dlut/article/details/111942170">数值分析常见基本算法及MATLAB代码总结</a></p><figure class="highlight"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">备注：正确与否不做保证，但尽力保证答案正确，仅供参考！！</span><br></pre></td></tr></table></figure><h4 id="3-2-线性规划模型的求解方法"><a href="#3-2-线性规划模型的求解方法" class="headerlink" title="3.2 线性规划模型的求解方法"></a>3.2 线性规划模型的求解方法</h4><h5 id="3-2-1【简答题】"><a href="#3-2-1【简答题】" class="headerlink" title="3.2.1【简答题】"></a>3.2.1【简答题】</h5><blockquote><p>用MATLAB求解线性规划问题：</p><p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAABkCAYAAABkW8nwAAAAAXNSR0IArs4c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%0A53+ugqlMe8AgPwAAAABJRU5ErkJggg==" alt="图"></p></blockquote><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">%1、初始化输入参数</span></span><br><span class="line">c=[<span class="number">-5</span>,<span class="number">-4</span>,<span class="number">-6</span>]</span><br><span class="line">A=[<span class="number">1</span>,<span class="number">-1</span>,<span class="number">1</span>;<span class="number">3</span>,<span class="number">2</span>,<span class="number">4</span>;<span class="number">3</span>,<span class="number">2</span>,<span class="number">0</span>]</span><br><span class="line">b=[<span class="number">20</span>,<span class="number">42</span>,<span class="number">3</span>]</span><br><span class="line">lb=[<span class="number">0</span>,<span class="number">0</span>,<span class="number">0</span>]</span><br><span class="line">ub=[]</span><br><span class="line"><span class="comment">%2、使用linprog求解</span></span><br><span class="line">[x,fval]=linprog(c,A,b,[],[],lb,ub)</span><br></pre></td></tr></table></figure><p>答案：</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">x=[<span class="number">0</span>,<span class="number">1.5</span>,<span class="number">9.75</span>] </span><br><span class="line">fval=<span class="number">-64.5</span></span><br></pre></td></tr></table></figure><h5 id="3-2-2【简答题】"><a href="#3-2-2【简答题】" class="headerlink" title="3.2.2【简答题】"></a>3.2.2【简答题】</h5><blockquote><p>用MATLAB求解线性规划问题：</p><p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJIAAAB2CAYAAAAjrsoaAAAAAXNSR0IArs4c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%0AJPNMb6SAc4SkPA4VkiLA9/t/gISZUXjQIbQAAAAASUVORK5CYII=" alt="图"></p></blockquote><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">%1、初始化输入参数</span></span><br><span class="line">c=[<span class="number">4</span>,<span class="number">-2</span>,<span class="number">1</span>]</span><br><span class="line">A=[<span class="number">2</span>,<span class="number">-1</span>,<span class="number">1</span>;<span class="number">-8</span>,<span class="number">2</span>,<span class="number">-2</span>;<span class="number">-2</span>,<span class="number">0</span>,<span class="number">1</span>;<span class="number">1</span>,<span class="number">1</span>,<span class="number">0</span>]</span><br><span class="line">b=[<span class="number">12</span>,<span class="number">8</span>,<span class="number">3</span>,<span class="number">7</span>]</span><br><span class="line">lb=[<span class="number">0</span>,<span class="number">0</span>,<span class="number">0</span>]</span><br><span class="line">ub=[]</span><br><span class="line"><span class="comment">%2、使用linprog求解</span></span><br><span class="line">[x,fval]=linprog(c,A,b,[],[],lb,ub)</span><br></pre></td></tr></table></figure><p>答案：</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">x=[<span class="number">0</span>,<span class="number">7</span>,<span class="number">3</span>] </span><br><span class="line">fval=<span class="number">-11</span></span><br></pre></td></tr></table></figure><figure class="highlight"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">备注：正确与否不做保证，但尽力保证答案正确，仅供参考！！</span><br></pre></td></tr></table></figure><h4 id="3-3-非线性规划模型求解方法"><a href="#3-3-非线性规划模型求解方法" class="headerlink" title="3.3 非线性规划模型求解方法"></a>3.3 非线性规划模型求解方法</h4><blockquote><p>编程求解下列非线性数学规划模型：</p><p><img 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7WS11a81C3vJrR5ohb6bc3WBggEtFGwXkfxEevvXPfsu/tA6X8QfB%0APh/S1uNevNZ/sxJbia70y6RJGVV3sZ5EEb5J4IY57Zr0TXoJNW0G+s45jC11%0AbyQhuyllK5I9s5qp8P8AQpPBPgXSNHkuDcnTbOK2aTG0OUULkDt0p8yt5gdF%0AJL5i/Ng45GeeRXhWt614w8JftX+FbrxNZaHqGh67HPpmly2Jk8zSpNvmMzbs%0Abi4QKTjpjHfPpvxL0zX9f8IzW/hnW4PD+sb0eK7mtFuo8A5KMhPRuhI5HbB5%0ArH0DwBq2q+JNK1rxdqWm6lf6Kkn2OHTbSS2tYncbXkIkkdmYrkDkABjwTzRG%0AVkOxm/Gf4reKvAHjrw49jDaT6Tq2oHSRYyoFknlZCUmMn8KBh0A5UHuRXW2/%0AgO31vQoU8ZDRfE13DM7JcT2EaRxhmO1FQlgMAhc5ycZrjPGfwHv/AB/Zw3Gq%0AeJmk17TtSS+0u+SyVY9PVH3CMRBsNkcMxOW46YFQ/HHQLXw/8OPDtjvkuJI/%0AEWnbZZT880hulZnOOMscn+lG9kgPYLG2g0mxitbSGG3tYFCRxRoFWNRwAAOA%0ABVXxT4im8PaJNeW+m32rzR422lpt86XnHy7iq+/JHSiK53ovzfWpPO/Ws+od%0ADw/SfjdrjfHXxBNH8OfFUky6baRtbiS1WaJd0hVmzKFw2eMMehr3jR7+S/0m%0A3nkhktZJow7wyYLREj7pxxkdOK5vTvBdvpvjnVNcWaZ7nVIYYZI2xsjEQYLt%0A787jnNdAk5UVo7dCTB+Lngm++IfhS80ux1680JriNlaS3RWL5HQk87T3xXP/%0AAAA8Ra74y+ANot5JDb6hbyS6fJdoPLVoopGj81cDGSq8HpnnpxVqH4RXcfif%0AUr5fGnij7Dqjl5NOaWN4YsjBEbMpeMeykVtah8M9Pu9E0vTbea70/TdMYYtr%0AWXy47lAMeXJ/eU9SO5+pp8yDU89/Z41W68Z2morqnii9uNP8Pa9LZaeUucPe%0AbTuHmv1kGGwBnoOc167pXxK0PxHrU2m2Op2dzfW673gST94FzgsB3APGRwDX%0APaJ8H9B8L+KNQ1m3jkhbUG8+aDf/AKOr7NhkCdAxUYJ9KxPBPjbw78QPihFJ%0AYyr5mi28tpZwrburKpI8x2YgAA4AUZ9TSclcqx6zFdblFYXjXVfE1pMv9hWe%0AlXUXlkyG7uWhZT6KAprRifDE/pUsu24jKt/ECDiiL1FynnP7ON/4ubwdpvnW%0AuiDSZJpnaQXDm4ALsT8u3bndnv0r12O+B3c84zz2rB8L6Ha+FNHh0+xTybW3%0ABCLnOBnPX61d1LT4Na064tZ95huEMcm1yrYPoRyPqKbd2I8u1by7P4/wWP8A%0Aa12sOpadLJqm1zsnaM52g/w8Ht0GfWvRYPiFofgPRLOFTcR2KxqVKwvIsEZ+%0A6znB2g+pqCb4Y6FeWunwyWKuNLJNuxdty565OctnvnOaofETxXb2d2mitZ6g%0A0N9GDcz21o0qhAf9XkAjLdMnoKq6egHpNveLMiyK25WGQRTL17prKX7G0Md1%0Aj5DKpZAfcCqWnOotIVVTGoQAKR90Y6Vcil2moTA4GePxZP8AFmH/AErSFuId%0APJDeUxj2FgCMZzuyAc5r03QpbqOwT7c0L3GMSGIEIT7Z5qhFYW41L7b5a/av%0AL8nzO+zOcfnWhHPxVcwrGT8UVgu/CV75zlmWBzFEp+YydVIxzxj8KPhpeWsm%0AgWOoTSSXWoSWaPJLyzYOPlx+HStKPQ7M6o94YEa4mXYznnI9KWystP8ABeky%0AfZbUw26nJWJCxGfQU+YLGxoXiiHXBMsazRyWz+XLHKm1kOM9PpzxWqNQjhKh%0AnVSxwAT1rj/BVjLa32qS+ZNJbXMwkhaUYc5UZ98emaueLPCsfimSx3GRZLWZ%0AZUkVsbcEEj8cUw1sdhb3HHeumtZc20f+6P5Vx8MuF/rXTWjD7LH838A7e1AW%0AOFvLjMz/AO8eaozXHFOvp8Tyf7x/nVGebmgZJNMADVaWf5TTJ5siqs83vUsC%0ASWfg1Xa4wKjklzVeScjp0qQJ2uNoz+lQS3OBioTPz7VDLNQUl3JmnzWb4ksF%0A8QaJeWLTXFul7A8Blt5DHNEGUruRhyrDOQw5BwakeYkVBLcY+gGaB2MrwT4J%0AtvBcUzLc3moXlwkUc15dvvmmWJNkakgAYAz2ySzEkkk1e1ezt9bsJ7W7hiuL%0AW4QpLFIMq49DTnnz/Womnz/hUtiih8OLe3jjUsyxqFBYlmOOOT1J96bJN71C%0A8/HWoXmqSiKy0i1029vLmGFVuNQdZLiUks0pUbVBJ/hUdFHAySBkkmZ58/8A%0A1zUUlzkf/XqFp8/41VybIkaWmNcc7v7tRSyMvXcPrxUBn4pXGWfN3D/Gl8/3%0Aqn5+f9mkM2KQy59oHqPr603zsCqZmyKGnxQBRPg/TB4yfXvJk/tZ7cWhm+0S%0AbfKByF2btmAST93OTWoZMDr9KqrKSW54z2oM3z89qALXmk4oE238KqiXB+tH%0AnZNArFxLggdvantJlfr61SEh/WnGX8TQMsibaOapa1oOm+JoY49SsLO+jhcS%0ARrcRLIqOOjAEcEeo5pzy7Y2Zm2heT6VQ8M+MtJ8XwTTaTqmn6pFbyGGV7O5S%0AdYnHVGKk4Yeh5oJZtpJtCgdvTtUnnZ71R807akSQbaBloT5b8MfWpRcqKogn%0ANPWXFFwsXknz3qSKXis9JDn0qRZ/0oGaKT7x14qWNlGOF49qz45938xU0cua%0ABWLyT8VIk2fwqiJKlimLUCNBJvlqVZ8iqKTZ/wA9aljm/wA+lVcNC/HNxVmK%0Abn6VmpLg1NFPiqJNFLnJqaOfis2Kfmpkn20AakU9WI5hn1rLjmyKsRXFAGpD%0AP71ahuOP0rJimyasw3FAGtHNirMVxismGf3q1DPx1oA1o5uP5V1VnJ/okXX7%0Ag7e1cTHN/KutsnH2OH/cHf2q7geeX037+Q/7R/nVWeXFOvnzNJ/vGqcsvBqQ%0ACV6qyy4olkNVZZ8/nSAfNNmq0su2myy4NQSyYHX6UFIV59oHtXP6H8SdH8Wa%0AxeWOm3X26awYpcSQxs0ETggFDLjZvBPKg7hzxwao/GzxFc+Fvg74q1S0k8u6%0A0/R7u5hcHlHSF2Vh9CAfwpvws8PW3gz4WeH9PsYlWCy06BVVRjeTGpY/7zEk%0An3NO2lx9TopZaheTJNeGaJ8QPHuvfF/xR4Lm1jSdPezgs9b+3G3SVtIgm3br%0APZkCRsoB5jdAXbHMYHtLXkZmZFZfMChim75guSAcfUHn2NK1twuTSy/KRniq%0A/n54qOaTI9fXNfPv7Xfxi1jSPDqaba+CfGc0Nv4h0n/iZW6wfZrpFvYJCkWJ%0AvMZpMGNVKgFmweOSlFydkK9lqfQjy+9ef/Fr4u6r4NvV07w34S1LxnrRg+1z%0AW1tcxWsdpBuKq8kshwC7K4RVBLbG6AZrV+HXju98c2F1cXnhnXvC7wz+WsGq%0ArCJJRgHevlSONvJHJByDx3pPH3jfT/hrok+qXMMk00zxwQW1tGGutTuDlYbe%0AMfxOxJCgnABJJChiFs9SlqYv7P3x70/9oPwTNqtrY32k3mn3sum6lp94B59h%0AdR43xtjg8MpB44YcA5A7LUI/ttnNCks1u00bIJYiN8ZYY3LnIyM5FeX/AAl+%0AFmufDP4WapHH9jHjbxdqkusalIrCS3s7q6kUMyg48xLePAA48wxdt1cX+y94%0A58YfGvTpDr3ijTZ9J8L6zqPh68EVtA8ni949675CAFhVVZTshX5yjkkIQKco%0A9UT1Oq+A/hvw/pPxA1y/8LTQw6DeWsVtHEt4Z31S4ikk8+/+ZmZgS6R+aeZC%0AhblSjN6szAH2rl/Anw68HeA3vpPCuh+G9JklcW94+mWsMLEpnEchjGcruPyt%0A0zW875GM4zUyd2USyzhBTWnznHPrXy9+0b8dfE3/AAsD4exr8LPG3k6f4qaa%0AE+fYltUKWlygWJROcEqxf59oCqeh4r3z4d+LtS8ZeGVvtS8O6p4TunldDYah%0ALBLcKqnAYmF3TDDkfNke1OUbJMXWx0Rn2/jSGUkfz5rxD9t/4i+JfhL8KZ/F%0Ami61Fotn4ZeC8mjbyv8AickzKrWjF1OxCmeVwxJ4ICnPUeBtZuPAfh/Vte8c%0A+NtNk+1SRTzRzyQWmn6BvRdtvG5wxU5GGlYsxOeAQKnpcZ6MXxnFJ5/FVLPU%0AYdStI5reaOaCZQ6SRsGR1PIII4INUfFut3mheHLq80/S7jWry3TdDZQypFJc%0AtkfKGchR65JxU63sBrDUIzceSJE87bu2bhu2+uOuKm8zOOea+T7P40fEI/tY%0A6heL8INWkuU8MW8DWY16y8yNDcSsJC+7ZtJ3KADnKknqK+ntE1G5v9GtJrqz%0AbT7qaFHmtmcSNbuQCyFl4baeMjg44qpRcWJM8r+MXxw+JOhWmqav4J8G6Hrf%0Ah3Qw/wBpl1DUngur7yziT7PGqkYGCAzH5iMgY6998E/i5Y/G/wCFmieKtPhm%0AtrXWrYTrDNjzITyGQ44O1gRkdcVi/GLXrrVNLm8H6Ay/2/r1syF9uU0y2b5H%0AuHx6AkIvVm9gSM/xH4F/4Qv4ER+G/DusSeHdM0TTntpNRhuFjntFii+XaxBA%0AdmwWbqOe5p6NJdR67nonil9Pl8M3w1Zok00wt9qMr7UEYHzZPp6/lXm/7Puu%0A+C/GXjjxF4k8J3+hytqkFvC9rpkiExRQ7wkkyp92R9zcMM4UDnBrlf2RvE+r%0AfFT4Y+GvGXi7xctzLdaXLGdNjdI7G4ijYI1xMrDLS5XLMCFXfjGOvsHgDxN4%0AZ8VadNd+GbvQ760WUxSy6a8bosgAyrFP4gCOD2IpSVtATOjE2P8ACo9Q1q10%0AeFZbu4htY2dY1eVwilmOFXJ7kkADvTDJ81fO37V3xD8f/wBkWNhF4DtfsMvi%0AKxjtbv8At6L/AEllnVowybMoGYAZJO3uDRG70A+lkm5z7U9Zdx7GuN+GHibx%0AJ4hsbhvEnhyDw5PEwWGOLUlvhKuPvFlVdpB4xVn4kWcuq+Ebq3GpyaPatG5u%0A7yKXypYIgp5Rv4TnHPpnvR1sB1M00gUCNQxzg57VYjfH4V84/sgeI9Q8f/Dr%0Aw34k8ReL7q8ubE3lnaxJOY47qOB2iaW4B/1kmFDFjwMg8Hk+2eEviNofjlrp%0AdH1Sz1FrFxHcLBIGaBiMgMvUZHIz1FHK0wOmWXHNSJPxyccc89KpJLx6183f%0AHe3XxZ8aLi0j+JmnW7Wu1W8J6xPJY2s+VBG2VCrPng/xDsR1FVGN3YD3fxZ8%0AePCfgiRo9Q16wjuV/wCXeN/OmP0RMt+lb/gnxdD410CHUreG8t4bjOxbuBoZ%0AcA4yUbkZ6j2NeJ+C/GDfB2JY9Q+EtxplvwTqHh4RalC3qxI2y/8AjpPWvZvB%0AfjCz8b+G7TVrDzvsl4u+PzoWhkHYhkYBlOR0IzRJW2A1NVnvItPkazW3a4VS%0AUEzFUz7kAmuQ/Z0+J2pfEb4Zya1rS2sNyLy5jYQAiJEjkZBjPXhetdZqV4tt%0Ap1xIzbVjiZifTg15F+y1DJefskR/Z2ZpLqC/kjI6ktLKRj604xTV/Ml7nReF%0A/iB4g+LPg3VvEmjX8On2sEkw0qBoRItysRILSE84cqRgYwPWuz+DvxLj+Knw%0A603XI4/JN7H+8iDbvKkUlXXPswIrzL9ljV4NC/Y702eSTH2KwuftDE9HVpA+%0Affdmtr9jzQbjw3+z9okd0GWS7M15tP8AAJZGcD8iKqdkB69HLg89TU0cx/8A%0Ar1nw3CuvysGA4yDVhJuODSQi9HISasQzY5rPjkx71YjlxQI0oZdtWIpazo5e%0AasxS0AaUMpNWYZc1mwyc1ailoA0Y5cj6V19nN/ocP3fuD19K4iGTiuysZP8A%0AQoeP4F/lVcwHnd62bmTn+I1Ukk+ZvrT7+6VbqTn+I/zqlLdBe/XpUjEnlyMe%0A35VUnk5NPuJ1C9frVOS6XYfmwPegpDnlwtQSS80x7lfXp71CJ943flQM4r9p%0Ay8W0/Z18cs7bVGhXgJ9MxMM/rW2dLm1LwBDp8N5caZNLp6Qx3Vvt862YxgB1%0A3AjcOoyCK4/9r+/W1/Zj8dlj97Rp1H1ZcD+ddtZzeRpVqrcHykHH+6KOgupw%0Adv8As2aNZ+OrfxB9u1R742a2mp7pExrZWZZ1e4wuSwdQAFwNihPuDbWf4Eub%0AQ/tW/EdYfs4uP7H0YzBCN7Pm8zu7khdo55A2jpivTXnqk81vbXO4JGssv8QQ%0Abm6d+vpSbYWLEsmUrm/iH4Fs/iLpdlY30lxHDZ6jaamvksFLSW06TIpyD8pZ%0AFBHXGcYPNbVzebcKe5qBpNzc/lUX1uBO0275ux5ry34zfs2/8Lk8Z6ZrTeNv%0AGnh6fR4Xis4tGuo7eOAvkSSDKMd7rhS2fujAwC2fSDLgD+lN+0+9HNbVD3PM%0AdG/Zdj0bQJrNvHHju9ur69SfUdRudRV72/t0jaMWTSbMpbgO5xHtYM7MGDHN%0AXPhr+zd4f+E3jHVNU0mS8htdQupLy20oeWlhpUsscccrwIqggssarySFG4KB%0AubPfPcYNRyXG4ckcdqXtGFjyb9kWXT/+EZ8ZLpv2NbVfGOrhIrZl2RqLggYC%0A8AHbkdsGvWPM4B9u1VI/IsA3lxQxBsA+WgXdgcZx6dKJbkMB2HtRzNu4Ix/G%0APgSz8Z+IPDt9dSXEcnhq/bULdY2AWSRoZIcPwTjbKxwCOQO2Qd4zZFVGuVB5%0ANI10FWk7gcN8SP2cNI+LtzrjeItQ1jVLDV7JrODT5JlFrpRZNjywJt4lYfxt%0AkjJAxuOec8d2/gn9mX4U2beI7yHWJFuPLtrnxBOjNfXki+WrSEqEAVABu24R%0AF4GevrhueF5qC7hhvNvnQQzbc7fMjDbc9ev4UczSsByn7N+m6PofwU0G00PU%0ALfVNLgt9sN3bAi3mO47jFn/lnuJC442gYrti3NVoXEUQChVVegHAFKswb3pO%0A/UDPg8E2Nr8QbrxKvnf2jeWMWnuCw8sRxu7rgYzkmQ5JPp6VtJc5H6VWM2BS%0AGfBpAeO+Jv2GtD8VeOtX8RSeNvidY6lrkge6/s/xFJZxMF4RQsYACqDgDt+J%0AJ67RP2ctC0W00G0F94gurDQ0ZRaXWoyTRalIzbzNdBuZ5N3OWPU5xXbLOc9v%0A8Kd5245/Kq9pJ6Aec6N8GvBP7PXgbWbiZ7ptCiMl1Il5L50dlF5hmMUSgDCG%0AQltvOScegqv+zX4k8L+ONQ8TeI/D91Zzza7dxS3kVopEdttjCxqxwAZSoBbG%0AeT6Yr052EoIZQwPUHoajjSOHISNUX0UYo5tNQLqyc/55rL8X+DrHxxaWUV95%0ArLp99DfwiN9v72Jty59RntVtbkf4U7zsnmknYC4sm0VheJ/h5Z+Mdc02+vLj%0AUMaaWxaR3LJa3OSCPNjHEm0gEbuhrTR6kSbHrTjIDj/CXwY8IfBG11zUraOS%0A0sbqWa9uVuJmlgtA53S+Wp4jUnkgd/pWf8GIWu/i74v1e1vbHWNH1a2sntb+%0A3iEe3aJF8jK8OqDawPX5yDXohlDqQRkHgg9DT4diBdirGPRRgVTk3uGheSTB%0Aql4k8HaN43szbazpWnarbkfcurdZR/48KkjnB9asLJzQB5vN+yrpWiTmbwjr%0AniXwXN1WPT70yWYP/XvLujx9AK9I8IWN7pHh+1t9Rv8A+1L6GMJPd+UIvtDD%0A+IovCk+g4qQS7qmV6L33APEGjR+J9Kks5prqGKYFXMEhjYgjBGRz37Vn/C/4%0Aa6b8I/DcOj6L9qj023J8qGaYyiLJyQpPIGTnGa1Y5uP/AK9Sxz/IaA8jmx8D%0A9BZby3WO5h0zULg3VzpyTFbWWRiCx2ejHkgcEk8V2Bgji01oFVo4RHsAj4Kj%0AGOMfpUEc5J4//VUqSbgBTv1F5HI/AP4YzfCzRNTt3vL66h1C/e7hS6l8ySBC%0AANvt0zgcc16FHLjiqMUmDipkkyKExF6OXJqxE/8AOs+KTbVmCTHfmrBo0IJe%0AKswyYNZ8UnNWoZOKCS9DL0FW4JMGs6GXFWoJf1oA0YpcCuzsZ/8AQoeR9xf5%0AVwkT8V29g3+gw/8AXNe3tQB5fqb/AOlSDn75x+dZtxLsPcD1q5qXy3Un++ef%0ASs2VvM2nHfrWgBJKTbN+vNU5n+X6dqszFkhP9309KozknGM4xU3QxkkmN/y5%0A4/OiKbdHwCOBxUNwzIG246U7zB5Awe1ToUjzH9sebzP2Y/GSt917Hax7kF0G%0APxzXfytts8M2fKAA/AYrzD9taVk/Zk8TA5xJ9lj46nddwD+tenXSAiZf7rEG%0Aq6C6gX8+3HbjFZt3iC4Tb/DkdatSP5EHy7m9sVTuX3ure9SUMlbfOu7duY8Z%0APSpgdvf86pSzqLlXG8bR0xUwn8wN97uenaolcBzyfL1/+vVG9l3yr821eT9T%0AVjzln4DfUelNcB4wvGMdxUKVgZWiuW8l2H8OQKpy3gkhXazeax5PpXHftF6U%0ANQ8Ey3E3iLxJodlZRSkQaFN5F5qV0wC28SsAXY7shYkI3sw3ZAxV/wCGVjrW%0AmfDTQV8TXEd14jjsLcanIgAR7nYvmkbePv56DHpgVppa4vI6GWVpZdvZRzUM%0Aly0DFeW4ytOWXyrv/eFV3Yz3TKNxC+nagY4M09uG3srdSM02S4aTa3zYz1FM%0AjBmTacA9KnuI9kKjcfl9KGTZgqMki/MTu6irG/OO461BGVmXPOKV5Ao9vSsm%0AzQi1a++zRr82C52gUyO4+z7fm4bg1HfqHuY2PzL0z6Utw+VG0bjmr6El4PgU%0ArTBapythepHuDVfUHdraRYZFSYoRGW+6GxwT7ZqeUDWjlBp/mdO/evJPhNZe%0AJNF+L+tWt14p1DxRof2KKSX7XBEq2F6XbdHCyIvylNpKtnbxzya9S+04+9RY%0AB0t9skxx81NjvfM+Vuuao3U226HPfjNK0pSZWPOeB6VXKgNCa48uNmz0FJYX%0AXmRAsct/Sq1xIZHCr16mnoyxOv8AdxigDRD7FqRZOcVAg4+b8KkU8+tSAxpm%0Aabrt2/rVlLlhbs1QmFZGzjpTpZVt4+ePSmgJY55E2Nu+8elWWlYpuDfhWcgk%0AEWWwAtVPGfjrSfAHh2TVNa1C10vTYSFkuJ22opJAAq0B0FpdMThufSpzOS5C%0AmuO+HPxg8L/FG5uV8O6xa6t9iUNNJb5Mce7plsYyajtfjh4Z1TXbeyt9Xt2k%0Au52tIHwyxXEy5zGkhG1m4PAOeKfKDO4gutzkenWpIp2kk46VUtZQYW/vDqal%0AtmY4bOKQi6LkrF83yn0p63bRKv8AEW44qtI/7rtSQAmQM3TsDTshGxBLhcVZ%0AjkqhC/GasI2agCwLomQqKs2kxbrVBfkf9KtQnAFaB0NKOTGKsQS1lh2wMVYi%0AnZcdKCTVhfjPvVmGTFZ0EnHNW4n5FAF5bjYvvXcadPnT4Plb/Vr/ACrz5n3Y%0APpXdaeQdPg4/5Zr29qrlA83v0JuZOf42/maz5ImX860b9t11J/vn+dUZeTil%0AcqxXmy6bW/SqxXYe5+tWZX5z+lVpXz1Oam4yvdR5x3/CoJYi0LBTtx0qxM3+%0AfSq8j4UilzDPI/2143H7Nusq3O+80yMf7ROo2w/WvSPFE11pej6jNZ2jahdw%0ARSSQ2yuENzIASE3HhctgZPTOa87/AG0WD/Ap425E2uaJGQRnOdVtBj8eld38%0AQfD994r8OXVjp+t33h68mkQpf2ccUk0IWRWYKsqsh3KChyp4YnqKd9Cep4j8%0AIPiX8RfHnxB8TeE77U/DscngDxBHDq2qJYnbq1tNFHLHawRb/wB0wDSAysWI%0ACRjaxLke1xKrOysys0ZGQD93PPPpxjrXnnhD9lPQfAfxPvvEWn3mpLa6g1pd%0AT6bIytDPfW8Usa3jtje8hEzu24kNLiQ/MorK/Zoa1/4WV8bFt/IwvjcAiIg8%0A/wBlafuz77y2c98+9EmnsEbrc9UuLXzTlcKx718x/H79rSHT/iL8O7GHR/iR%0AptvZ+KZBqOzQLpF1OFLK8URRhQftAaXy3CDsm84C5H1A7cn29+9cf48+GUPj%0Ajxh4P1Z7qS1bwjqUuppGqgi6L2k9tsJz8oHnFsgE5XHGciIySepVrjvhj44g%0A+IvhiPV7Wx1jT7e4d0EWqWEljcjaxUkxSAMAccEjkHIrzn9rP9oTxN8AoNJ1%0ArTdFh1Hw7a6lZWerI0bNdX/2uXyUjtMMFDodrMX4YyIox85X2hmyDubnpXi/%0Ax4/ZYu/2hLLW7TXvFNxJYvNFc+HbWOySOHQZk2N5zYObiTKMAzkbFlkCgE5B%0AFq/vbA0zQuPgjdfGrRIm+Kllpkl9p99Ld6baaHqF5bx6ZG8YVVeRXQzTqDIp%0AlCqpDHaqgnPeeGfClj4Q8P2el6fC1vY6fEsEEZcvtVemWbJY9ySSSSSSTXjP%0A7UXhGbwj8ALGO61K6vtUvPGOi3N3eLI1uLqebVLYP8itgRBTtWMlgFRc7mG4%0A+6z3ALNt9eKJN2vcZyXxd+J2g/CfRYdQ16S+jt5pvJjNpYT3jl9pblYUZgMA%0A8kY/EivFf2Y/2zvC3jPxN4i0m41jXrrUL7xXdW+lw3Gi3vyW5KCJGYw7Ylzu%0AIEhUgHkCvpJJ/LLcsPXB61xvwl+G7/De28QRve/bP7c1y81kkLt8rz3DBPcg%0AAc0RkrNMdnc6XU/Lt7eW4bcohRpG2ruYgAk4Hc8dK8I0H9pDx94p+JHibwTb%0AeENITxJpZtNQtpLi6kSzt9NuACpuXALfaRh12IMEjqApJ9s8WjVZvDN4uhzW%0AFtrDR4tJb2NpLeN8jBdVIZl9gQa8g0n9lfXdF+Ndz4sj8VFv+EmsLa38Sna6%0AzXElvKZI/suDtijIYx4OSE9WJJmMujCx7XGvkxhc5P8AOm3K+aNuSvPavHdK%0A8CLp37R9rqWi65rN01qlyPEr3GpSS2szSKptrZYSTGkiY3DywCqL82TJz7Dv%0Az/jUy0H5njX7V/7VPhT4MeAPFWmyeJrfSvFdro8s9jAEZ5xKYyYio2lSS2MA%0A9a6H9n39oTwr8atBtYtF8SWWu6lb2UU14sOQyEgAsQQP4sjj9K1vjl8PJPip%0A8IfEfh20e0gutasJLRJp03IhYYyeM8V0Ph7R4dB022t4oYI2hhSIlIwudox2%0AFVzLlt1Ah8deILjwz4deWysW1HUJGENpbZ2iaVvuhmx8qjkk9gD3ryn4AftE%0A6/8AtF6DYz2/hX+yo4J7nTfEU8l2V/s25j3qVtsp+/wwU7sAAN6givRfiKvi%0Am9Fhb+HJNMtY7iUx6hc3O4y2sWOHhUDDSZ7NxXm3wG/Z08SfB6HXdFXXo4/D%0Ax1G9v9LmRjJdM10wc+cGAU7HMhGOpYenJpy+YuU7b4TfAay+Ed9cSWmt+KNS%0AW43AQ6lqTXEMJZizMq4GGJPJOeAO1ddetJJPtXKrivNfgT4WvvDvj7xS9v4i%0A1zXPC8i26Wo1S7+1Ml2u8XDROfm8o/IMfdDK2Mc59SaFXBz696QO7PPdW/aS%0A+H/h66n0/VPGXhywvrNzHLDPfRpJEw6hgTkEe9bngH4l+H/itFNL4f1zS9ci%0As2CTNZXKzLExGQG2k4J681qz+DNGu5mkm0nS5ZW5Z3tI2Zj3JJGataXodhoa%0AMLGys7NZDlhbwLEGPuFAzV3VtA5TnPip4q1DwH4WvL7TdPbWNTELva2Yk8vz%0AyilzlsHaMDGcdSB3rm/gh8frz49WGj6rpHh+4Tw3fWjPc3884ja3uhjMCxkZ%0AkCtvUuMDK8ZHNdV8RLLxJr91b6VpS2drpN9C6X2otL/pFrnjEUeCGJGfmJGM%0A9DXCfst/B3xh8JvhuvhfV761gs9GNxb6fPbv5slzHJM0iyuCMKyqwUL2wT3x%0ARzaCsezM7fZtufmqey3LHhj82a8j/Z98SX03xH+IWi319r00ej3ls1pbauoM%0A8MUkOS6SAfNE7q20HJUqw6EV64p2Acc5qXvYHHU8F+Ln7btx4A+J8vhWHwvJ%0AprRNtGta/ObLS5fdZAG3D8vrXR6Z4M8Y/GTTBcX3xJsbfT5AD5fhWFFU9f8A%0Alu5dvyA/lT/HPwR8aT6rfXnhvxzHLa3jmRtE8Q6bHf6eM/wqw2yKue24gV5P%0ArPwjuvC17Lc618KtT0W43b5Nb+HOrNFuP942+5G/DDfjW3u2uhdT6q8NeHP+%0AEd0Gx09bm6uUsolh825k8yaUKMZdu7Hua89/ad+Fnjrx6miXngzVNAgl0OSS%0A4aw1W1M1vqDlcBW7DHY9ic16F4PKt4U0zE13cA2sZEl0u24k+UcyDAw/qMda%0A5XxP8R/Gnhz4nNp9v4Hl1rwzNbq0Gp2d9EJY5e6yRuRhc9wScdjUxk07hy3P%0ACtf/AGotSvv2PfiY83h6Lwf428KONJ1S1tUCxJLKVRZoyOQrBj1yRjqRgntf%0A2ofCNv4I/YCu7azUQtoWnWVxayJ/rIZo2jKyA/3t3OfetvVf2XZfH/w4+I1n%0Aq0sNrrHxGkFxK0Z3JZMkaJAmf4guwEnuWbtSeLPBvij42fCTRfBOraPLpDSv%0AbJ4guWkVoTFCQWEBB+bzGRcZAwrHIBGKvnWhKiz1r4e6jPrvgbR726UpdX1j%0ADNMuMYdo1LfqTWvCskPycNiuc+Jfi64+Gvw31HVrDSbnWptLg3x2NucSShcA%0A4+gyfwqb4R+P1+KPw10PxF9iuNP/ALas47v7NN/rINwztP0/Wo6FHRbHZN1T%0ARBmK7uKA2BjrUivlvx6UuYViyn3fpU1u5zzVaJs1YjPPFFxkoLuat2wZOtV4%0AuRxxViNsYp8wix5e5etTQwZbrUMTZqzFVXFYuwHpUpfkVWibC/55qbZuNBJc%0AiO4H9K7nTj/xL4OT/q1/lXDQD5K7zT0zp8Hy/wDLNf5VXMB5vet/pMn++T+t%0AUZ+tW784uZf95v51SnqSyGVsVTnkwDViVye9VZgD/WpYyNj3z+VQOd1SNzn/%0AADmo2OOKkDyL9tDMnwl0uLPNx4s0BPTP/E0tj/TP4V6tNJudvTP515R+2MjX%0AXgXwvCrKv2jxtoCc/wDYQiP9M16m7/Mfrn603siepXkARmwfvHJ9u1Ubaxt7%0AKSSSC3htzIcuY0CmQ89cDnqevrVyQ4c+lV5QFGPzNQUQsePx5qGRsEn8sU+V%0Asg/XFV7mXyV/vc8VIxH681HvIaiRgsoH5UyRsHP4UBczvE3hnTfGFlHb6rp1%0AnqUMUizJHdQrKqOucOAwOGHY9RVroFHQLxinv0x6moZDjLfzoGgd/wDJ71GT%0AgUjswY9OlRtLyaBi5Un6Uhlw3X+tND5X603dz/WkBx3h/wDZ88F+F/HU3iaw%0A8PWdvr1w7yS3wZ2kd3GGY7mIyRxnGccV2LPx9KRjzSYxS8xgHUf1oMwB4qOb%0A6/8A16aKaAmL5XrUOo2Eeq6ZNazGTyrhDG5RzG2D1ww5H1FOY03eGPrTEzl/%0Ahd8EPD/wcE0egw30MdwoVlnvprlUA6BBIxCD2XFddkjJzUXm4bpml8zPajUG%0AyQFs07zM00nIpDzQJSJ1fd71W8QaQNe0W6sWuLqz+1IY/OtZPLmiz/EjdiKk%0AU7B+NSp+9IqugX7GN4L8BWfg1rmZZrq+v77Z9pvLpw80+0YXJAAGB2AA5NdD%0AG/NV1bZUqnC0w9Swr5NTRvxVaI4FTBuCfSgCZDhutSq+O9Vg3HzVIr4FKzDm%0ALUcmBj8KmD4bn61UV/mqVZeTmjlFcNV02PW7OS2m8zyZcBwrlSw9Mjt6+tWr%0AKCOwt44YY0jhhUIiKMKgHQAVDG+RUyvzVCJ1bFTRn8arA4xU8T9aALKNk1Yj%0AbDfyqrG1TxttH496ALMb81ZjbiqkbGrEZ5oEW4n4FWY24FU4zmrEPAqxNl6F%0AsgVaiORVGLpVuA/LTJLsTYWu70450+3/AOua9vauCiPyn867zTT/AMS635/5%0AZr/IUAea3xxdS/75/HmqNwcDpV+/G2eXv8zfzqjIKCypL/Lmq8zZ/Pmrcq4H%0A41VmGD6evtWYyCRtik+lV3Of73NTuNrH61CyYH4UAeRftZjfpfw9j6LJ8QND%0AB98Ts381Fdt8R/iBa/DXwtNql1b3l6d6QW1nZw+ddX87nakMSfxOx9wAAzMQ%0AqkjiP2rV/wBL+FMW3In+I2kjk/3Y7l//AGWvT9Qs7WXybi4jgkNgxniklA/0%0AdtjIXBP3fkdwTx8rMOhNN7E9Tyf4P/tT2PxV+JWqeDdS8O+IvBni3SbRdSbT%0ANYjjD3NqW2edG8bMjKGIB578ZwceluTn9K8o+G3hP/haf7R2qfFqSNrfRU0J%0APC/hjepWS/tTMbi4vyvURySEJEDy0cZkwBItcf8A8Lk+KesfH/xF8PoG8N6Z%0AqGpeHbfxTpV3c23mxeF7Vp54HguUWQG5mZkgGQyqGeZvuoqsSjd+6NeZ9ASD%0Adxn3NV2bLfy96mdlVwhZTJjJ7bh3OKrszbmG3bjo3rWQxjNg4qJz81fPX7a/%0A7Xmk/DXwHrel6feeJtN8RafqWmxG5g0a78lEa8tzLtn8sxMphMinaxJyVHzE%0ACvXvhh8ZPD/xktby68Py300NjKsUputPuLJlLDcMLMiFuO4BHbrVunJR5mHo%0AdGTkHiuf+IGnapqugNDpurxaDI0itcX5hSZ7aAZLlA/ybyBgFwVXJODjFcr+%0A1N8a9W+A3wv1DX9G0KPXJNHtpdTv0mmaGKCygAaZtwU5kIICL3+Yk4U1nw2+%0AoftUeCL6HUrPUfDPg3Wora60qe0vvJ1S+iJ3kzJsIhRsIQuSWVsMBkrS5dLl%0ALsa37Peua54i+F0F3r9w19O93dLaXjQCCS/sxM4t52jGApkjCtwACCDgZwO1%0AY4XHesrwD4ITwF4cXT11TWNY2yNIbvVLo3Nw5Y9C2BwOgAAAFWvEniLT/COh%0A3GpapeWun6fZp5k9zcSiOKJfVmPAH1qdwLDNkUN/kV4X4d/bR8H6x+0L4g0U%0A+OPCo8P6fo9jcW0jX0SK9xJJOJQJCcNhUi+UdM+9e5pKt1bLJGylZF3I685B%0A6EfzqpRa3DQ4rWv2iPAvh3xxH4XvvF/hu18RTOsaafLqEa3Bdui7Cc7j2HU8%0AV2Wdy5HT+dfNv7bv7OnhXVP2Z7zwrZaLZz+KvE15Hb6PdiBTfvfu+83PmAb8%0AoAzs2eAK9L+Jfxpsf2a/h/pcmtx6hqUdnFbx6hcwqGFrESsbXMxJ4QMcnqcA%0A8cU+VNK24XPRyu6mgbOf0rmvhx8T4fia19Naabqlvp0BT7LfXMYSHUlZd2+H%0AncVHTLAZrpWOB+pqLDGs2aRT+tEgUIWY4Veck1xlv8VTcfHibwmsdq1rDose%0AqfaBJ84d5nTYR0xhM5otcR2pXA9qarZHpTlfcny859O9cZ4z+P8A4V+H3jjT%0AvD2sakLG+1NZGhaRCIN0cfmMjSfdDbAWCk5I96ASO0xtHFOQ5NYfgHx3p/xI%0A0D+0tN+0rbedJB+/geFiyMVJ2sAcccHoRyK3IxjFBJIqZWnL8prM8XeMdL8A%0A+H7jVta1C10zTbUZmuLhwkcY9ya83sf2vNH8aRBvBmi+IvGSMSEuLGzMdo5/%0A67SbVx7jNOMZWuVZ9D2BMP8A5609Gwa4D4c+IPiB4g8SCTX9B0PQdFaJtsUd%0A81zeF8jaThQgGM5GTzivQcrEMkjavJJ6Cnrewh+3A3Hj0p5+UZr5r+O3xP8A%0AEPiD42fCePSbp7PwjqHif7M5QlZNTMcTOW/65ZGAP4gCfSuj03xbN8c/2rPF%0AXhia6uofDvw/s7dZLe3naL7ZeXALbnKkEhEUgDpls88Yrle4HuYHHvTkGK8Z%0A/Zh+IupXfxD+JXgTVLubUW8D6pF9gu5zumls7hC8aOf4mRlkXceSMZ5BJ9oX%0AlaVnsxEiv8o5pyyZNQr91u1OjOHpgW4jUqVDEcVIGoAseaQcVMjZ9feqyrvx%0A9c1ZQYX60AWIz2qZDlqrRHaOKnQ+9Ai0jZFWIGzVWDg81YT7mc9qpC6FqI1a%0AhbuKpwE8VaibIqiS1C2G/lVuE81TiPP9fSrMbfOKALkTZFd9pxP9nwf9c1/l%0AXn8fIrvtOBOn2/8A1zX+VAHnuoL/AKTL7Mf51TmiIxkEc8cVR+LN9/Z3gTxF%0AP/aCaT5NjcyLfOhZbMhGIlIHJCfewOeK8f8AgHDrngrx5cWPiTwvo+gNc6Q1%0AxDqOiXvmaXqaRvHumaNiSkuGU+Y3Lq3PQUct1crm1PZJlwDWDpnivT9c1vVt%0AOtbpZ73Q5Y4b+IKw+zvJGJUBJGDlGVuCevar3h3xNp/jTR7fUtKvrXUtNvF3%0AwXNtIJIphkjKsODggj8K8Y/Z50Gx0z4xfGLU1aeNo9fjh3SXkpjVBaRsxKs5%0AU8knJGVHAwoAByb3DmPZZDiq8jHA/LNGm6vZ69pEF9Y3VrfWV0gkhuLeVZYp%0AlPQqykgj3BpJGBfGV5OBk8VDC7PIv2o9snin4NxsA3/FwrR+exWxv2yPxAro%0Avj98Hovj38Obrwvc65rWhWF+4+2PpjRpLdRjOYWLq37tjjcABuA2n5SQeH0z%0AxGv7Ufxy0W5sYZbLw38K9RnurqO+AhvdQ1NoZLeD/RyfNihjSSZw8oXzGKbV%0AKgsfaZiI49x6CnK6sC1PHPDv7LOpeH9QlvLj4nePtduobG5t9MOpzQSw6Vcz%0AQtCLxI1jVWljR5AofKjzG4rPuP2MNLuvHth4lufEGsXWpyaZLpPiKWWOLf4p%0AgkmhmZZyqjYu6BIwqAAQ5jAA5r2ppFKe9QyncB0XAqeeW5Vjxfw7Hbn9v3xh%0AIsimZvAulGVfM3HzDf32TjPB2KnAxxtPfJ9Ylz+tMbSLODUWvks7Vb2UbGuB%0AEolYccF8ZxwO/YUsjYP9alu5XKcf8a/hsfi54E/sP7a2np/aWn37S+X5m4Wt%0A7DdbMZH3vJ257bs84xXUPK0km5mPryabdSHzFVfTJoK5BA/E0tdg9DzX44/B%0AzVPjtbat4b1DVraz8D6vpL2tza28TrfXFwwcfNLu2+R80ZKAZYoQTtYiuR1j%0AwD4m+EP7Hni6PVvEstzrmk+FZLe2u9Od7ZLZbW0ZY3jzkrIzAuzDnJAHCivd%0AX4Ydax/GOgWfi3QbnSdStY7yw1CNobiBydsyEYKnBGQehHfNNTa0D0Knw+eS%0AX4e6G0sjSTNp1szuxyzsYlyST1yec1oajZQ6lZvDcQw3EMgAeOVA6tjnkHg/%0A/Wp2nabDo2nQ2tupjt7dBHGpYttUdBk805myuRxUgeW+HP2cdJ0744+LPEVx%0AougyabrVhYWtrEbSNmjeHzfMYgrhd29enXb7CvTiPstrtSMYjXCqPl6dhThy%0AfrTPO/fbe6+lAeR81rL8etN+Jupa9cfD3wZrkkzvBpTP4jaEaXafwpt8o/O3%0A3nYdeB0UZ6H9o74b+PPjp8H/ABL4MWGx03+0NH8s6qsyM2oXBXPkLHj92m/g%0AsSeDwOte6NwM96juJNqjp1rR1NbpBujzPw1p3ja4+GcH9pX1n4RnVE84JHHc%0ANp8McYHyn7pZmG45yADitr4D6zr/AIi+FumXfiTy21aTzN8iReULiMSMIpNn%0A8JeMIxXsSRVz4m/D9fif4fXTpNU1TS4llWVnsZFRpNvIB3A5XjpWh4K8MHwj%0A4dgsWv77VGh3brm7YNNKSSckgAd8DAHGKzvpYCbxF4ftfFWhXWnahCtxY3iG%0AKaIkrvX0yMH8jXgdn+wl4Ll/aE1XUJPDs0ei/wBh28FuUvp0UzGWUygEPn7v%0Al5GcdMV9FOQ3AO38KRUb7xx+FNSa2Boi0bSodH022srdSlvaxrDEpYttVRgD%0AJ54A6mvmv9prRtY+KknhbWNP8G339k+EfGFtf6hBcW2by+VXxJLGgJ3IoCsO%0A5I6cc/TQbnp1FDS7TzSjKzuHoeXfFf4meJvAPwqbX4YdDsbqCMTfZL52AmJf%0ACwBhwjbON5yAewFen6XcNd2MMzLtMkYfHXGQDXL/ABL8Baj8QtOutNW/sYdD%0A1K0e1u4JbXzJju4LI2cA7TjkcHnNdPpFjDommW1nCNsVrEsMYz0VQAP5UDML%0A4v8Ahu98V+ALyysNN0PWLiQr/oWrZ+zXCjqCQCQfQ49a+aYtEm+Eu5ZtF+In%0AwpWE7zNos51nRH7n91htozyRsXvX18DmnKnX0rSM7KwjyH9m/wCJeteNtauI%0A5vGPhHxho8cRZJrCFrXUIJNwwssJJULgnnjntXoPxa8H33xD+HeqaLpupf2P%0Ad6hGIlu/L8zYuRuGMjqOPxq5ZeCNH0zXJNVtdKsLfUp4zFJdRQKksq5zhmAy%0AeQOtbKhgi/Wk3rdB5nyX8dfhJ8Trb4pfCGzj8XaNcSW2q3DWckejbIrIpbHl%0A0DYZSuV6jBOa7H4F+Hrj4P8A7WnxKg8Q3EazeMLSx1i3vCvlwXPlq8c4XPTa%0AzLxnODX0KhSeVflVtnQkZx9Kj1HR7PWkVby1trpVOUEsQfYfbIq1NtWYjxn9%0AlPwfdXPxR+Kfju4hkt7Txdq0UWmh+DLa2yFVlA9GZ3I9hmvVNG+J2ga74w1H%0Aw7Z6tZXGuaSgkvLFJQZrdT0LL1A962hEtrEsaKqrjAVRgAVyWh/BXSdJ+Mmp%0AeOBa20WtahZDTjJCu1mhDBiX9WYquT6KB65nmvuKyOzxmP8A2qci+tGd35VI%0ANvX+dAEsQxUyHJNQhix/rUsZ3f4UATxjI9qljG41HFw1WIk4JoAkiG4fjU0Y%0AwajiHHy1NEv86AJ4xk1Zj6VDAMGpoF4oEWIxtNWYl/8A1VBCMCrMQ3CtCCeA%0A5b19KsQjnmo4U+X0qxCvNAFiEcV32mgf2fb/APXNf5VwcQyP0rvNOb/iXwfL%0A/wAs17+1AHmmuWEOppdW9xDHcW9xvjlilQOkqnIKsp4II4IPBrC0L4faD4RF%0Awul6Ppunx3QPnJb26xrIP7pAGNvXjpzXUX0WbqT/AHz/ADqnNGAO1BWph6B4%0Ac07wfpUOm6VZ2unafaqVhtraMRxRAkkhVHA5J/Ouef4PeF7fxHq2srotn/aW%0AuoY9QlO5hdqY/LbcmdmSnykgAkdScnPZSQgGq0kW/JP5UcwjnfDPhDS/AHhS%0Ay0XRrGHTtL01PKtraLOyJck4GST1JOTzk1YkjDNz7fjWjPAD9c5qA2/GcZqX%0AIrY8Z+NS/wDCO/tP/CHVbNfKvNam1PQdQKHH2u0FjJdIr/3vLlhVlz03Nj7x%0Ar1a4G+DBHUcmvMfjXafaP2mvgjGwUr9v1uXk8/LpTj/2avVJI/kPp/OiWyFE%0AzvJVB/tY49qpXjt5h9B0Gag+JWm2d14I1Rb/AFafQtOWHzr2/iuvszW9uhDy%0A/vePLBjVlLghlViVKsARxn7Pfhe40fw/rXmXWoNpN9q0l7olnfXL3F3p2nvF%0AEscchkJkXfIk0yo5LIsyqcFSonTdlHalG2MG6Y4OelQpFvTB/hNX2t9kO0Co%0ABFsPHToakZXki2n7v0qE5VuemOKsucZ5qvcwGXGNwAPbvUgRtyOM+tQtHvOe%0A61YwEH97b61DIMbcMd3U8dqBkco+U+vpWeZzmRCrLsPDHo2a0nXcBjOKqy2y%0Aylm9elAEcK4XrnvTvK+cN0oitSsv3uAKzfE/jfRvB0kI1fVNO0z7SSIvtVyk%0APmH23EZoBGgxyf8ACmSRBj82euajMn2toZIXEkTAMCpypqdzhefWgCpcMWba%0Avyg02ORlb+9UskJZ89AKaLfa2cZpiITNuPpR9qMhx93B5qT7Mwc7f4jnmnfY%0A18rnqfajQNSMXW7OOxqZwSnPWkjtlib6Cnhhj1pBqOUfL9KamfM5pyvx9ajI%0AZmP8qBkzArjb3NODsrfjimFd6rTypUDFBJIZPm/Qe1OidmUVGY8/NjtUkCbe%0A1V0AniTYOKlXoPWmocD8KbHcsz9KNivImaPc2c96WfKqMfLngUq8gU2ZWY/L%0A271XmSCZyoqaNWkY56Z4pIYcgflU0Q2t7UASxQtuNWIYdme/9KSFcj171PjD%0AcdKAGn5Zh6dKtRdOc1X27n+gq0q7Up6CJI5MtgdqsJz0qtbAE5X8auRDcaQy%0AWIfLViFP1qGNatR8ECgRJDwatIMnioY1qzEuSKsWhPCMirEK8fpioYl4q1EO%0AKZJNEnFd9pw/4l9v93/Vr29q4WNciu801v8AiX2/P/LNf5UAefX0W65m/wB8%0A/wA6pTL81aV+mLmT/eP8zVOVM/nUyLM6VaryD8utXZ48DjtVWZOakZUlTJqK%0AWLcOvt1qxIKif7v480ahc8e+LkDH9q74LNu+VI/ERIx/05Qj+tdL8bV1uT4e%0AahHoOpQ6HdGGV5dVfyz/AGZEkTyGULIChJKKnzcKJGfnZg4XxHxJ+1v8KV2s%0AzR6T4ifg8D5LFcn88fie9dT4z+Gdj431/R7y+utUaHSHd/7PiuzHY6gzNE6/%0AaYhxMEaJWUNwDuyCCRVW2uSeQfs6apq37T3wg0PxB49vNDvtD8UeH1Sbwu1j%0AFJFcSRSRrJfSyMcndLHIfKChEWSMHLKSfUfh74C8M+BPD3k+E9L0fS9Jvn+1%0AhdNiRIbhmUL5nycNlVUZ9AK5jwx8A/Cv7Pvw18QR2bXU+mx6fdKP7SlWdLC0%0A3z3Jto/lGIVknlb5tzHcMsQqhY/2MreO2/ZB+F8MUiyfZ/C2nxSYOSki26B0%0APoyuGUg8gqQeRRLVXQzsvGHiGPwj4cvtUlttQvI7CIytBY2zXFxLjska8s3s%0AK+c9E/a+0+L9pnxTJeWPxEh0dfDelC30+Xw5es8E/wBovzLL5CoSm9PJUOR8%0A/lkDOw19PXB2LXI6T8O00T4teIfFq3csk3iDTrDT2tigCwLaNcsGDZySxuWz%0AnGAg9amLSTuM3ImF2quuQsgBG4YOCM8+nWvDfEH7SHinTfjlN4LtvClpcXWv%0AaLNqfhnzJ5Ii5gnMMpvW2nyoz8sgKgna6rgscD3TUlkkspPJZY5NhCsw3Kjd%0AiRxkA9s814Xq/wCyn4m1n4ieH/Gk3jKI+LLfT7vSNXvIrExxTWlwkahbWLeR%0AC0bJvUktl3Zj2FFPl+0Vc9f0/wC0NpVub5Io7toUNwsTFo0kwNwUnBKhs4JH%0ASnqnzbq8r+K+lXXhX48/Dm8tW1YaffarJYXdzHqTGMg2U/lWr22dpjLIrmTl%0AlZf9o161jn2WoYjmfiP4tuvA/hiTULPQ9W8RzRyKv2HTQhuHBOCw3so469en%0ArXntl+05qV3q9razfCv4kWa3UyxGaSyhaOHLBdzFZThR1JGeK9J8f+C18c6E%0AbE6pq+jt5iyC50258icY7bsHg9xiuK0z9nG+0nVbe4j+JHxEmjilWV7e4vYZ%0AY5gGB2NmLO04wcEHnrVx5bageiJHnkDG7ivMfjV8CPAXiXQvEeuePNL03WrX%0A7I5llv4RILKBU+7Hn7vrxyWOa9WCfPnvivC/jrrfxDufiXb29j8Om8TeEdPC%0AzIE1eG2N5cAgh5Ebqq/wqeM8noMTG99PzsO5c/YR8Ca18Pf2W/C+m621011H%0AFJJDHcEtNb27yO0ETE85SIopz0xivWmTK/rWF8Ldc1/xF4VS78SaRFoGoTSO%0ARpqTi4NrGCQoaRflZiBk44BOO1YHwL+Pcfxv/wCEmT+wdY0G58L6rJpNxFfx%0AhTMyqrrImOqsrqQfeh3bbJO2xzThu/4CKVQCKeg3D6dqkZGFwv1prgM30NTu%0Am5fw61Eo/nQA3Zy3ajyM4296lRcyYqUrhOlAiuY8DvTlj29vapCKXpQAwRY+%0AtPWPH+FOXg09F4+tMBoTcafGm3tTgmaeifNxTAaq7R/SnJDT0TFSpHkVIxsa%0AZ9amEfH1/SlWP5fxp6jP0q0ISOPI/GpY48yU6GPC+lTRpx/SmA6Neacibc/W%0AnRp8tSpGaAEiTJ/nUyw76WNOTUsfyrQA6GLYP1qaOMony+nFCjA6d6sxjigB%0AYU2qvNWIo+lNii5qxGuBTQh0akn8KtRLk9KjhTmrEaZqxEsKZxVmBc4qOJM1%0AaiTkf40Eksa8fpXd6f8A8eEH/XNf5VxESfLXd6cp/s+3/wCua/yoA4LUEInl%0A/wB4/wA6oSZYmta+T/SZf94/zqnNFQWZdyMnGKqSphvwrUuIt1VJ4dvT60Em%0AaY+D61Xkhz3P51oGHDc1Xlh20DPIPF8Syftl+A1zl7bwrrcoGOm6fT0r0mYE%0At6DPWuB1S18/9tTQ/vH7P4H1Bh6KXv7QZ/8AHa9FuLXA27ufaiVgiUJVyvfr%0AgVDLuVz83v0q5PZlh97b/SoXtipbryTj2rMooudys35VGIdyAn6/WrX2PYAv%0A8K9KRY9i/hxSYFKeLch9OmKriHMfU8Gr8yZX9ajFt8lSBy1p8OtD0zxbda1D%0AptumrXTeZLcAEszFdpbBOAxUAZABIGK2Xh4PappIGWTNP8vA+nY0gMm9Uq/T%0ANLbhmXkY5q5cWvnHPG2mRw+UMVTegyB48L+Peqkke+4b1FaDxj0+lVZF2yNx%0A16UREVWhJJyfxqjpHhyz8O20kdpH5bXMrTysfmeV26sx6knj8q1jH833e1E0%0AeItx+9/OqDUzzlUNOso23ct16VMbdmTGPxoiTyvmJ9vrQA6SNigC9aqMSW+h%0ArTCrs3fdqo8TBtwHvUoAhiK9adIdpqWL5xTZ0LL8o/GpArxPvk4qxEhYbj/+%0Aqo0iK9F+arEC7vb696rlAj2lT601G2/hVi6YKvzfhUaxgr7nvRYCROVoPB9O%0A9CxkhVB4qQR598VVkII+XFWIwKhdMJ0p0G532/w9qOXqMmBBHr2p8UoRM0yN%0Adp2n5jU6Qlh0HWq5QJkjyF9e/tQkhZqhWVogVbqf5VLA+5un0o5SSxHuNWkT%0AK89ahjTJqZWwmfzp8oXJAADViJN9Qr03VYgb5RnvS5QuSImAKtRKAOwFQhcA%0AY45qaI7jj2p8ork8IwKsxpkVViJUVagfcmafKFyxCnFWIk/z7VXjOw+/86tQ%0AnmiwixFHirEK/wCNQwpwKtwJkdKAJI1xXb6fKRYQfL/yzX+VcTswK7Swj/0G%0AHr/q17e1Vygcnew7ppP944/OqdxD1/WtW9T9/J/vGqU8WKkdzLljqvNDkGtG%0AZd1VpoqTHuZ0kXy/pUDw/wD1q0JIRg8VCbf/AD6VBR5G9s0v7a0fzfLB4BY7%0AcdC+prz/AOOEV32rajaaLbLNeXVrZwtIsKyXEqxKzscKuWIG5jgAdSelcXbw%0AA/tl6jKc5j8CWyHHIG7Ubg/+y1yf7cSaL4t+FviTQRG2r+J7XQbjUbPS48Fr%0AZOX+3kHhTGLd1Vs5yxRQWlAOnLdpE7LQ9b8+OW7mhWSNpoNokQMC0e4ZG4dR%0AnnGetRTRZNeU/Dv4ieEfh78DNX8fWen32oQrp9pf6zq8Ecct1rs5tlneR5CV%0AMjI05Ri2AjFkAAQhfYJYNo5G1vT+6amUbMaM94cH+QrN0zW7HW7u9htbq3uZ%0ANMn+zXiRuGa2l2K+xx/C2x0bB7MDS/EKDxH/AGF/xS76IuqCZSf7WWVrcxc7%0Ah+7O4N0x24NeIfs5S/EhvHfxCmlsvAb2Vx41kj1Zo7m6SVHjtLKKQ24KEMvl%0AqpUOVJctnAwaFHRsZ7J4s1uPwl4ZvtUuIri4SxhaUxQLullI6RovdmOFA9SK%0A8db9o/xl4F+LvhTw/wCPPBNjo2k+OLtrDTNQ07VftptbnBZILhdi4Zh3XK5D%0AEEgGvfmQAfNxjk5rynWPDUPxo+Jmg+J7144/B3ga4kutMeQ7U1TUGUwi6yeB%0AFEruqH+N3LD5VUsRt9oD0KSIc1G8H4+teL/G7x34y8F/tFeAdL0zWLOOHxtN%0Ae6X/AGXMoaC0jjhWWO84Adpfll+XIU/KvZmPpPhfxJpvhnS9E0bUvFtlrGrX%0AkWLe4uZ4YrjVcE/OqLgN6fIMcVLg1qBtNHmoJXjSeOFpI1lkBKRlhuYD0HU4%0ArSaKvO/2gPD3w71PS7Ofx5daTp/2Qs1ldz35sriAnG7ypFZXHQZCnnA9KlK7%0AsB2Rt8/h29KbJajJzXzOPifq3hqFl+FXinxX8Ro1bEem6jo0l9aPjjYuoYj2%0AD3Zn7V9BfDDXdb8U+CLG+8Q6LH4f1iYN9osEuVuVgIYgfOvByMH2ziqnTcQD%0AxZ4q0vwJoc2paxf2mm6fbqWknuJAiKPx/pUug6xa+KdCs9RsJVuLG+hW4t5Q%0AOJEYZVh9Qc1yv7WdvCv7NHjqSa3hnji0S6YCRQw/1ZrW+CGn/YPgv4TgXgQ6%0APaoB9IlFTb3bgbbRbT2pptlYcrVxoN3pR5P8qkCm9uCdw7fpTPI/WrrwYH9K%0AQQc09QKYg2nHSnmLFWjF8386BFk07AVFt9rfXrUoh2/41MIsineTmi1gKN5a%0AbgG9OaNgZQFXt1q9LFvQ/kKjSBlxxxVAQ2MQMWGqcWx3cL1p/wBl2ybl/EVY%0AiTd/X2qmBVeEk9KckAY9KsTp5aZ98fWrEUOFFHMBnrCXuMr/AA9RVg7owox9%0Afap7eEGdvWpXiw1VcllNoPOnVu2Og71YgtcJ83r1qZrXYBnj6VMkONvpT9Qu%0ARxw4TrU0cC7PbpUvkZVvb2qW1h3p1+7TuSQiH5xkdPWrMEP7ypRAMirEMI8z%0A2pXAY0e2nwrn61I8WWA9/wA6ljgw3HWqQCCPcoq1BBiP0ohg/d/j+VXILf5f%0A1pgQJEzMv97pV2GP5eaIocHFWI4sjGOtACJGzFVz1wavwptX1qGCLaV45xV6%0AKLFADUj3V22nofsEP/XNf5VyUceV6V2lhH/oMPH/ACzX+VAHJ30R89/941Tu%0AIsmtXUFCzt/tMce/NU3gLD7uKloDLliwW/pVO4Xnp1OK1pbYiqdxbYP3T+VS%0ABnzLg4FV3UqrflkVoSwYxx7VG1sEG7HSgDynSLUv+1t4lkx93wppiE+mbq9I%0A/rXfTxbTuH3ugPT3rjvC8Jk/aV8dNj/VaPo8WfbN239eld3JD8lDHE81/aC+%0AGt58QvgL4k8L6Jb2cVxq9kbOBHYQQxBmBJJAOABnoK64yvPGsjRtEzDJjYhi%0AhPUEjg46ccVqGIgfjVExZkYMOVOPrR5DsVZhxyPes7R/DVjoLXhsbOG1/tC6%0Ae9ufLXHnzvjfI3+0cDP0rZeLhvl+lRiHa3IqRowvFng+z8Z+Hb7S9QSWWx1K%0ABre4SOd4WeM8EB0IZcjjgjivOPCv7FPw78D69p+oabpOpQyaVMk9tFJrN5Nb%0Aq6/dJheUxtjggEEAgHqBXsbw4Pp/hULJzjp+FLma2YzyvXf2WfDOvWVms0+v%0AfbtP1b+2LfURqUjX0cuHURiZst5QSR0CdFB4wfmrjfjXaeA4fEWl+CrhbLS7%0Aya5069ec2skkiJbTIbeCFlBxIxQKACMK7Hktz9BtBgVC8ZPSiMpIFo7lG4Qh%0AicZ56eteafGn4d6/418R6XcaHaeB45LOGRf7T1rTGvruzYkYECZUAHGT8w5A%0A616m8XNV2g5pRbWqA8gH7Mmo+KFVfFnxC8Xa1H/HaWLppNm3tthHmY+shrv/%0AAAR4E034e+HoNJ0mBrfT7PIijaV5SMkkks5LMSSTkknmugSHH5UFOaJSk9AP%0APf2jfh/q/wAUfgz4h8M6LJp8N5r9lJZGa8ZgkIcY3YUEn6VreAtHv9F8G6Vp%0A95FbQ3Gn20ds/kSmWM7FC5BIU847gV1Dp6CmiHj7tGtrAeY+Cvid4g1v42+L%0AfDGq+Ho7HS9HhgudO1GK480XiSFhhx/A/wApO3rjnoQT6AseV+tFtoNrYyzN%0ADDDC1w5llZFAMrnjc3qcAcmphDtzRp0Ah8sF/pSNFg/yqXyM5P8Ak014jvAo%0A5RXIxCM/Wgg7+mKseQXZeMU6SEAr6Z5osMgEORSrHk1ZMG1d3YdKYgy+MfnQ%0ABD5OG/D0pwi4qXYWkwOtSrBsI70+ViuQpBx0pyQ4NSKmakjj8wUWYXI1h3eh%0AFSrHtFSxW/FSrDxSGVo4l3/7TVM0HmOoqZLfLZPbpTtvzVSAj+zK9TR24C9K%0AckW0dP8A69TRwYb1qibDIrfauMe1SxWoT0qWOPg1PDbUDI0twx6U9IMPuqeO%0AD+fWpFttz/SgghWAk1YS3wOKmigGc1YhhyelO7AhitWKY61YhjbyyB97HGam%0Ahhx+f51PHDxT3Arw27cce1W7e24qSGHJ9atQw4WqAjgtCrf55q3FHg9MinxR%0AZNWIIcnNADUjymMYrrrFMWUP+4vb2rnYrfiuwsoR9ih+79xf5UAcneW+24k9%0ANx5x71XaLf06da2LuDdK3+8apyW2PzpMDLkgyfpVSSHP/wBateaDk1Vlt+DU%0AAY89v861HJb1ozW3zfQ9qikgoA8v8DWin49/ESRc8RaTGfqIJD/JhXYX0O+P%0APft71y8sifDb4xa5f6lut9J8VwWjxXzL+4t7iBTE0MjdI9ylGUtgE7hnIxVr%0A4kfFjS/htq2m2+pR3C211PHBcXq7Rb6YZN/lGYkggOY36A4CknAxmnqNGyIT%0AtX6c+1VJ4CLhdv507wjrn/CU6M11/ZuqaWoneJYtQt/ImkVTxIEySFYHI3YP%0AqBVx7XzJs9lGOlSUZ81s20gfhmqcobsNvGTmqfxFPizT47W48K2Oh6pJGzG7%0As9RuZLZpk+Xb5Mqqyqw+bO8YORyMc8qP2hdN0K7Fv4x0fXfAlwzbfM1O383T%0A5G/2LyHfCRz1YofYUct9Rcx3Ah3Kp/lULxBW69KvabdWusafFdWVxb3drcLu%0AimglWSOQeqspIP1FeZ+KvEmoeMv2gY/BFhfXOlafpmjnV9UurUJ9pld3CQwo%0AzqwVerscZOFHAzmVG7sO53hi3r61G8GB/nmuH+AfjPUvEd34s8O65Kt5rPgj%0AV2017zYsbX9syiS3nZVAUSMjFW2gDchOBnFeiGDjdiiUbOzGncypotqfoKhF%0Apsj9/WtO6tMIrc9aje3x81SBmQMpcrjkdPei4tyyNx71ZS32TdPvVK8GVoAz%0AIoty89fSpHh2hvTpzViKDZGT680XFt5seecgZHNMnUpyW2B9elNFl2/Q1MI9%0A3zNwIxk+1YMfxk8L3AbydWjuWUlSsEMspBBwRhVPQ8UWYzWFoPOPtT/sgY/5%0A4rk7f49eHb64b7Guu6mqzGB3tNFu5kRwdrAsI8DByD6YNdyY949FocWtxlH7%0AJtNDWW9vxqhdeOdLez1qRLnzF8Pu0d9sXLQsqCRh7nawNauhX8GvaVb3lrIJ%0ALe6jWWN8EBlYAg8+oNKzQEb2p2fqKr3Fnu2q2Tk5PNbIgwMCqlxLbR6lFbyT%0ARrczqzxxFsNIo6keuMjpQBVS12YBP3RUkVqzNnt0rQWzVI/8adFD8n8+Ksmx%0An/YyrAD61LHCVG2rptyxz6UkFqBljzn2o8h2Ikh/lTo4Sfb6GpvusqgVPHB8%0AtLlAgji4p32fc9TLCyzYxwec1YjgyKLagV/svtUkaYFWhDtqSOD8aoZBDBuN%0ATrDhvwzU8UHH0qRYP0oIZDDHu6VMsODUsFt5Y471YSDFAiGO3+WpY4M1MIMD%0A6dalt49/biqsAkEGBUyQ5NTR2+cVMtv3xTsBFb2+Gq1FB/hT4bfJ/wDrVYit%0AicUwGRQcVaht+akit8VZgts0AMjhwtdVZpi0i/3B29qwY7f5K6e0gItY/wDc%0AH8qBamJd2/71/qapzQYrZuoMyN/vVUltsZoGZE1vxVWa34/zxWvNCBx1qvLb%0A4qeUDIlt85qCS29q1JLfiq7wdqkDKkt8H/69eZfGzwF4k+Iuj3lvZ2enxxaT%0Ae2t/pqG4DPqskTRviTcoEQX96oGTubaSVUHd61JBUElvQtHcDzbxrreveH9J%0A8PytJaw3Wq+Iba1uYzEJPKt5pj+6VuBuWMBS+DuOSNuRjrntflpvjDwPD4wX%0ATvPuLy3/ALLvY7+HyCnzSx52FtytkDJ4GM960Hg9sUFankv7QHjKXwcdLjh8%0AX2HhRrwy7g+kNqd3dhdn+pjB425+YlT95fx89uvD2peP4ZoW0f4seNo5kKyL%0ArOox+HNNkByP9UnluynB4KNkdff6WMG2TcPlbpkdahe24+hqlKysg5TivhN4%0AObwR8PdP0xtH0nQmtxJmx02VpbWDdIzDa7KpYnILEjlix964Kz0T/hDv2y9Y%0Avr1o4bXxh4ch+xTSNsjee2cCaEE9XEe2TGc7cnHBI9teHIqhrGgWev2TWt/Z%0A2l9bswfyrmFZUJHQ7WBGffHepTauwseP/s16I+p+LfiN4sQN/Z/ibXiunN1F%0AxBbr5fnA91Zy4HYhcjrXoum67puuR3MljqFjex2crQ3DQXCSLA45KOVJ2sAc%0AkHB5rfjs1t41VFWOOMAKFGAoHAAHtXEeBfgTovgv/hKTHp9rFH4vuWnvrOJm%0Aa32lShUBv72WZsADLnAAAqXruG2x45e6T5v7QOvaPceKNW/sHUPDZvtakt5p%0AIzcXMMzI/wBndWJiwsygrFz8irnIOfXLf4haBB4ug8Nf2kP7WdWjhjkST98Y%0A1DOiyMNjyKuCyhiwGSRWo3wc8MmbRWXRbGM+G1MemBFKLaKdpIVQQCMqpwwO%0ACoPUZrndUvU8QfFrT7G40PxBb2eiXUlxaTjTXFpc3LRMpnab7qqqu6jPLs5P%0AOADV0wVjrWsgfm2/NjtzWF49sdUufD00Ok3MOn3Mmc3jqri0QAkvtbgngDnj%0AnPao/j94DvviD8NLrStMvtU03Urh0Ntc2MvltHIDwZGyP3fdsc+mTgVc1r4c%0Af8JFBpaX2p6k8enx7bmCOUJBqZwv+vXBLAMucAjqQcgkGVbco87/AGcfF3ij%0A4qeDND8Rapeada2sltLby2kcAeS/ljcI1z5m4eWNyvhApADDJz09OtPLvIPM%0AjkSVGyAyMGU/iOK5vwb8EtB+FOjX0bXsj6SWm8qG+eNbewiml8x4lIA+RnP8%0ARJxhc4GKr/AnRG0eXxZbGDTY4l1t5oW05ibNkeKIjYv8BHRwMjfuOeacmnex%0AJ1iwKD6bulcN8X/Ht7pPhbXIPDixz6tp9nLLPcFtsGnBULfM3Qy9MJ2yGbAx%0An0XUdJTUrWSF2mjWRdpaKQxuB7MuCPqCDXL/ABM0e38LfBrXodP0/wDdR2Mq%0AR20EW7ezAj7vfJOT3PNK4zn/AIf3+i/DOw0vQfsb6PHfgNaTSP5kd7M43uGl%0A/wCezMWOGwW7Z6DrPGNzqWkeGrqfSNPOqaoqYtrbzFjDueASWIGBnJ55xWhc%0A+FLPWdBhs7+2iuoV8p/LkXI3oQyt9QwBqj8S54bXwldRTwa5NHeD7MF0iJpL%0ArLcfKV+77scACjm1DQ8R8zWNV8C3On2Nha6Xp2oSGyYXtwok1Wcyg3Vy0yBg%0AsZBMYbDZL8YAGfSNK17xNafEHStHutN0G10+4s57iQ21xJNJGIyioBlVAyW9%0AO1S+EPh7bWHgu8sbjQ7vTdL+wm2Q3l+1/feWQQQR84XaMEBXbp0AAp3w4ubH%0AxF8Q72bTb0alp+h6ZbaZHcCXzN8hzI+W7tt8vOec05C03N7xpBq0WgzS6PcW%0AVvdQq0hN1AZkdQpOAAy4JwOc149P4u1jxH8QPCd/9ukud1ldT28kXhW4+UP5%0AQ4Bck8H73AA9dwr27xhrdjpNg9tcNNJPfI0UNtbLvuZ8jB2L7Z+8cKO5FeJ2%0A/wADNe0zxxpMkmsa+xsdOkni0ddUkZVt1ZE8kzKVJkP3iVwu5VUDaM042tqF%0A2ei/CnWNQ8aaI2sT3zXFncFo4YZNO+xyRlHKMxG5jyV4rsYrbEY496z/AIb2%0Aujw+CrSLQvk02FSqRs7M0JydytuJYEHOQeRT/A/jnSfiJYXF1o10Ly1tbh7W%0ARgjLslT7ykMAe4/MGjzGXZbdRHRawbhmr/2bj/PNJDa7GoFcpyxbBnH51PDB%0A8np61ZFmG+8PwqVLbaOlAFJoMyL7VYSLJqaO15z61MbbaRQDKbwFen61Pbwb%0AO/61OtpuNTRWeDVEkaxHbwv6U5EORx07+tWkt8/nU62+0UgK0MW5uhFWI4Mi%0ApobYYqwkIPHpSAqmD5PrVi2tgEqYWvy9Kngt8pVAQCEq/tVyCDig2+7t3q5B%0ABiqArpBtcf3auRQYp8dtukH0q1Fb4xQAyKLBxViCLceKclvVq2tdooAIoMCu%0AitYiLaP/AHR/KsmC3x2roLeMi3j/AN0dqAMu6t8M31NUZocdRj+ldlPpsBB/%0AdrVOTTLf/nktAHISwZYcZ96rTRY/h/Gu0fSbdh/qVqvJo9rz+5WgDiZFUH7v%0AtUMtrx0rtX0W1P8AyxTpSNodpn/UJRYDgbi3IGRVeRfm5Fd9NodoU/1CVXbQ%0ArNjzbx/5FKwHCvb5qvNDtXNegf2BZhP+PeP/ADio5/D1kyf8e8fSoK6Hnzw5%0A5HeoJoNgr0QeHLHb/wAe8f61G/hyxDn/AEaOgVzzkx7xUb24I/x716JJ4ZsP%0Al/0WOox4bsSW/wBGj+UZHX1oHzHnctoG47VXmi8v7q/lXp3/AAjFh/z6x/rV%0AeXwxp/mf8esf60BzJnmyw7v8KSSEk+1ekr4V0/zv+PWPkZPWlbwpp2f+PWP9%0AaBPc8wePYOR+dIYOa9ObwnppH/HpF096htvCmnkt/osfB46+ppco7o8xu9Nh%0AvLdoZ4o5oZBtaORdyuPQg8EU210qHT7ZYYIY4YYxhY41CKo9ABx+VeqN4T03%0AaP8AQ4qjfwnpuP8Aj0i/WlYL6nmRtuaRbfPavTh4T07cR9ki/WlHhLTQP+PO%0ALp70WCyPL/IwOKBbfNXpZ8J6aJV/0SKpD4S00f8ALnF+tFgPMYrf5h7elUPD%0AvgvTvCovP7Os4bP7fcteTiNcebK2AzH3OB+VevHwnpo/5dI/1pV8J6bk/wCi%0ARdfenYOY8uj0mGK6kmWGNbiYBXkCDewHQE98VUHg23bxUNY/eG6W1+yAbvkC%0Ab9x49Scc+wr17/hEtN3f8ecXX3pR4T00H/jzipiPHdN+H+j6TrdzqVrptpbX%0A12SZ54o9ryk9S2Op9zWhb6ZFZReXDDFBHknaihV55PAr1L/hE9Nz/wAekX60%0Af8Inpuf+PSKgR5kLenC2ya9OXwlppX/jzi/WnDwnpoH/AB5w0AeZra4NOFvx%0A/SvTF8J6bj/j0i/Wnf8ACKad/wA+kX60Aeax2+01J9j3CvSB4U07bn7JF+tS%0AL4W08n/j1joA84itdtSpbGvRF8Lafn/j1jqRfC+n/wDPrH+tAHnsdvz0qZLb%0AJrvo/DNhgf6LHUsfhmw2/wDHrHQBwKQZPap44Mdq7pfDdjn/AI9o6lXw5Ygj%0A/R0/WnGzA4aO3yamjtNre1dvH4fs9/8Ax7x8U9dAs93/AB7x96sDjYrarEdv%0AzXYRaDZ/88EqVdDtAB+4SgDk4LXaasR2vFdXHotqD/qUqSPRrX/nilAHNQ2u%0ARzVqG056V0SaTbgf6lalGmW4P+qWgDBitvatyCH9wn+6KsQ6dB/zzWrawoFH%0Ayr09KtIm5//Z" alt="图"></p></blockquote><p>一、解题思路及步骤</p><p>1、由题知，求f(x)在约束条件下的最大值。可先将其转化为求最小值，再使用fmincon函数求解。所以函数f(x)的定义如下：</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">function</span> <span class="title">f</span>=<span class="title">fx</span><span class="params">(x)</span></span></span><br><span class="line"> f=-(<span class="number">2</span>*x(<span class="number">1</span>)+<span class="number">3</span>*x(<span class="number">1</span>)^<span class="number">2</span>+<span class="number">3</span>*x(<span class="number">2</span>)+x(<span class="number">2</span>)^<span class="number">2</span>+x(<span class="number">3</span>));</span><br><span class="line"><span class="keyword">end</span></span><br></pre></td></tr></table></figure><p>2、定义约束</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">function</span> <span class="params">[f,ceq]</span>=<span class="title">fceq</span><span class="params">(x)</span></span></span><br><span class="line"> <span class="comment">%不等式约束</span></span><br><span class="line"> f(<span class="number">1</span>)=x(<span class="number">1</span>)+<span class="number">2</span>*x(<span class="number">1</span>)^<span class="number">2</span>+x(<span class="number">2</span>)+<span class="number">2</span>*x(<span class="number">2</span>)^<span class="number">2</span>+x(<span class="number">3</span>)<span class="number">-10</span>;</span><br><span class="line"> f(<span class="number">2</span>)=x(<span class="number">1</span>)+x(<span class="number">1</span>)^<span class="number">2</span>+x(<span class="number">2</span>)+x(<span class="number">2</span>)^<span class="number">2</span>-x(<span class="number">3</span>)<span class="number">-50</span>;</span><br><span class="line"> f(<span class="number">3</span>)=<span class="number">2</span>*x(<span class="number">1</span>)+x(<span class="number">1</span>)^<span class="number">2</span>+<span class="number">2</span>*x(<span class="number">2</span>)+x(<span class="number">3</span>)<span class="number">-40</span>;</span><br><span class="line"> f(<span class="number">4</span>)=-(x(<span class="number">1</span>)+<span class="number">2</span>*x(<span class="number">2</span>)<span class="number">-1</span>);</span><br><span class="line"> <span class="comment">%等式约束</span></span><br><span class="line"> ceq=x(<span class="number">1</span>)^<span class="number">2</span>+x(<span class="number">3</span>);</span><br><span class="line"><span class="keyword">end</span></span><br></pre></td></tr></table></figure><p>3、使用fmincon函数求解</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">lb =[<span class="number">0</span>,-Inf,-Inf]</span><br><span class="line">ub=[Inf,Inf,Inf]</span><br><span class="line">[x,fval] =fmincon(<span class="string">'fx'</span>,[<span class="number">0</span>,<span class="number">0</span>,<span class="number">0</span>],[],[],[],[],lb,ub,<span class="string">'fceq'</span>)</span><br></pre></td></tr></table></figure><p>二、答案</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">x=[<span class="number">2.6667</span>,<span class="number">0.1667</span>,<span class="number">-7.1111</span>] </span><br><span class="line">fval=<span class="number">-20.0833</span></span><br></pre></td></tr></table></figure><p>参考链接：<a target="_blank" rel="noopener" href="https://blog.csdn.net/ten_sory/article/details/54571525">MATLAB规划问题——线性规划和非线性规划</a></p><h4 id="3-4-微分方程模型求解方法"><a href="#3-4-微分方程模型求解方法" class="headerlink" title="3.4 微分方程模型求解方法"></a>3.4 微分方程模型求解方法</h4><blockquote><p>计算下列微分方程组：</p><p><img 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rZ3n7yPPTeOPxoqhbykyxLn+IZ/Oiq3JMC+uP9Kk/3z/OqrTZ+bNV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%0A21l+9j4P3hRXWR+F4IiG3SHbz1opgf/Z" alt="img"></p></blockquote><p>求数值解：</p><p>1、微分方程组函数声明：</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">function</span> <span class="title">df</span>=<span class="title">yprime</span><span class="params">(t,f)</span></span></span><br><span class="line"> df=<span class="built_in">zeros</span>(<span class="number">5</span>,<span class="number">1</span>);<span class="comment">%u,v,w,y,z</span></span><br><span class="line"> df(<span class="number">1</span>)=<span class="number">0.5</span>*f(<span class="number">1</span>)*(f(<span class="number">3</span>)-f(<span class="number">1</span>))./f(<span class="number">2</span>);</span><br><span class="line"> df(<span class="number">2</span>)=<span class="number">-0.5</span>*(f(<span class="number">3</span>)-f(<span class="number">1</span>));</span><br><span class="line"> df(<span class="number">3</span>)=(<span class="number">0.9</span><span class="number">-1000</span>*(f(<span class="number">3</span>)-f(<span class="number">4</span>))<span class="number">-0.5</span>*f(<span class="number">3</span>)*(f(<span class="number">3</span>)-f(<span class="number">1</span>)))./f(<span class="number">5</span>);</span><br><span class="line"> df(<span class="number">4</span>)=<span class="number">-100</span>*(f(<span class="number">4</span>)-f(<span class="number">3</span>));</span><br><span class="line"> df(<span class="number">5</span>)=<span class="number">0.5</span>*(f(<span class="number">3</span>)-f(<span class="number">1</span>));</span><br><span class="line"> <span class="comment">% df=df(:);%变成列向量</span></span><br></pre></td></tr></table></figure><p>2、在边界条件中无z在t=0时的初值，使用“打靶法”找一个初值</p><p>3、使用ode45求得该微分方程组数值解</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">[t,Uvwyz]=ode45(@yprime,[<span class="number">0</span>,<span class="number">1</span>],[<span class="number">1</span>,<span class="number">1</span>,<span class="number">1</span>,<span class="number">-10</span>,<span class="number">5</span>]);</span><br></pre></td></tr></table></figure><p>4、为了便于观察，作图：</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><span class="line">subplot(<span class="number">3</span>,<span class="number">2</span>,<span class="number">1</span>)</span><br><span class="line"><span class="built_in">plot</span>(t,Uvwyz(:,<span class="number">1</span>),<span class="string">'-b'</span>)</span><br><span class="line"><span class="built_in">legend</span>(<span class="string">'u(t)'</span>)</span><br><span class="line">subplot(<span class="number">3</span>,<span class="number">2</span>,<span class="number">2</span>)</span><br><span class="line"><span class="built_in">plot</span>(t,Uvwyz(:,<span class="number">2</span>),<span class="string">'-r'</span>)</span><br><span class="line"><span class="built_in">legend</span>(<span class="string">'v(t)'</span>)</span><br><span class="line">subplot(<span class="number">3</span>,<span class="number">2</span>,<span class="number">3</span>)</span><br><span class="line"><span class="built_in">plot</span>(t,Uvwyz(:,<span class="number">3</span>),<span class="string">'-g'</span>)</span><br><span class="line"><span class="built_in">legend</span>(<span class="string">'w(t)'</span>)</span><br><span class="line">subplot(<span class="number">3</span>,<span class="number">2</span>,<span class="number">4</span>)</span><br><span class="line"><span class="built_in">plot</span>(t,Uvwyz(:,<span class="number">4</span>),<span class="string">'-k'</span>)</span><br><span class="line"><span class="built_in">legend</span>(<span class="string">'y(t)'</span>)</span><br><span class="line">subplot(<span class="number">3</span>,<span class="number">2</span>,<span class="number">5</span>)</span><br><span class="line"><span class="built_in">plot</span>(t,Uvwyz(:,<span class="number">5</span>),<span class="string">'-c'</span>)</span><br><span class="line"><span class="built_in">legend</span>(<span class="string">'z(t)'</span>)</span><br></pre></td></tr></table></figure><p>如图：</p><p><img 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alt="img"></p><p>参考链接：<a target="_blank" rel="noopener" href="https://blog.csdn.net/qq_18820125/article/details/104727013">Matlab求常微分方程组的数值解</a></p><p>（其他，与答案无关）</p><p>求解析解代码：</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">syms u(t) v(t) w(t) y(t) z(t) t;</span><br><span class="line">eqn=[diff(u)==<span class="number">0.5</span>*u*(w-u)/v,diff(v)==<span class="number">-0.5</span>*(w-u),diff(w)==(<span class="number">0.9</span><span class="number">-1000</span>*(w-y)<span class="number">-0.5</span>*w*(w-u))/z,diff(y)==<span class="number">-100</span>*(y-w),diff(z)==<span class="number">0.5</span>*(w-u),u(<span class="number">0</span>)==v(<span class="number">0</span>)==w(<span class="number">0</span>)==<span class="number">1</span>,z(<span class="number">0</span>)==<span class="number">-10</span>,w(<span class="number">1</span>)==y(<span class="number">1</span>)];</span><br><span class="line">S=dsolve(eqn,t);</span><br></pre></td></tr></table></figure><p>运行代码：找不到解析解</p><figure class="highlight"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">备注：正确与否不做保证，但尽力保证答案正确，仅供参考！！</span><br></pre></td></tr></table></figure><h4 id="3-5-统计模型求解方法"><a href="#3-5-统计模型求解方法" class="headerlink" title="3.5 统计模型求解方法"></a>3.5 统计模型求解方法</h4><blockquote><p>【简答题】</p><p>随机产生100个整数，绘制出其直方图，并用多项式拟合其统计图。</p></blockquote><p>1、生成1000,000个随机数（使用randn函数），并绘制直方图</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">A=<span class="built_in">randn</span>(<span class="number">1</span>,<span class="number">1000000</span>);</span><br><span class="line">hist(A,<span class="number">100000</span>)<span class="comment">%划分100,000个区间</span></span><br></pre></td></tr></table></figure><p>如图：</p><p><img 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f+gzd/8AftaP+FHab/0Gbv8A79rXqtFH1/E/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%0AigAooooAKKKKACiiigAooooAKKKKACiiigAooooA/9k="></p><p>2、获取直方图纵横坐标，使用polyfit进行多项式拟合。然后将拟合得到的多项式绘制出来。</p><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">[y,x]=hist(A,<span class="number">1000</span>);</span><br><span class="line">p=polyfit(x,y, <span class="number">8</span>);</span><br><span class="line">y1=polyval(p,x);</span><br><span class="line"><span class="built_in">plot</span>(x,y,<span class="string">'*'</span>,x,y1,<span class="string">'-'</span>)</span><br></pre></td></tr></table></figure><p>得到拟合后的多项式为：<br>$$<br>y=0.0002\cdot x^8-0.01\cdot x^6-0.0003\cdot x^5+0.1806\cdot x^4+0.0023\cdot x^3-1.3501\cdot x^2-0.0027\cdot x+3.5628<br>$$<br>绘制图形为：</p><p><img 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zPXGt38TXV9FFK/h2K4iTWfLb93JMucNtxyF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%0ARjHOeUvNW1DULx7y7vJ5bhlZTIznIU5yo9F5IwOOTXXRy2V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KKACiiigAooooAKKKKACiiigAooooA%0AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo%0AoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii%0AigAooooAKKKKAP/Z"></p></div><div 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