LeetCode cpp最新中文题解.pdf
LeetCode cpp最新中文题解.pdfLeetCode cpp最新中文题解.pdfLeetCode cpp最新中文题解.pdf目录3.4 Add binary615.1.5 Binary Tree Level Or-3.5 Longest Palindromic Substring. 62der traversal il3.6 Regular Expression Matching665.1.6 Binary Tree Zigzag3.7 Wildcard Matching67Level Order traversal. 963.8 Longest Common Prefix5.1.7 Recover Binary Search3. 9 Valid Number70Tree983.10 Integer to roman725. 1. 8 Same Tree3. 11 Roman to Integer735.1.9 Symmetric Tree1003.12 Count and Say745.1.10 Balanced Binary Tree.. 1023. 13 Anagrams755.1.11 Flatten Binary Tree to3. 14 Simplify Path76Linked List1033. 15 Length of Last Word775.1. 12 Populating Next RightPointers in each node ii 105第4章栈和队列7952二叉树的构建10641栈795.2.1 Construct Binary Tree4Valid Parentheses79from Preorder and In4.1.2 Longest valid Parenorder Traversa106theses805.2.2 Construct Binary Tree4.1.3 Largest Rectangle infrom Inorder and posHistogram82torder Traversal1074.1.4 Evaluate reverse pol-53二叉查找树108ish notation845.3. 1 Unique Binary Search4.2队列85Trees.1085.3.2 Unique Binary Search第5章树86Trees li.1105.1二叉树的遍历865.3.3 Validate Binary Search5.1.1 Binary Tree PreorderTreeTraversal865.3. 4 Convert Sorted array to5.1.2 Binary Tree InorderBinary Search Tree...112Traversal885.3.5 Convert Sorted List to5.1. 3 Binary Tree PostorderBinary Search Tree113Traversal9054二叉树的递归.1145. 1. 4 Binary Tree Level Or5.4.1 Minimum Depth of Bider traversal)2nary lree115目录5.4.2 Maximum Depth of Bi8.3.,2重新实现 next permunary Tree116tation1425.4.3 Path Sum11783.3递归.1435.4 4 Path Sum il118 8.4 Permutations II1445.4.5 Binary Tree Maximum8.4.1 next permutation... 144Path Suum11984.2重新实现 next permu5.4.6 Populating Next Righttation144Pointers in each node 120843递归1445.4.7 Sum Root to Leaf num8.5 Combinations146bers2185.1递归1468.5.2迭代147第6章排序1238.6 Letter Combinations of a phone6.1 Merge Sorted Array123umber1476.2 Merge Two Sorted Lists12486.1递归1486.3 Merge k Sorted Lists124862迭代96.4 Insertion Sort List125第9章广度优先搜索1506.5 Sort list1269.1 Word Ladder1506.6 First Missing Positive1279.2 Word Ladder il1546.7 Sort Colors1289.3 Surrounded regions162第7章查找94小结16413194.l适用场景1647.1 Search for a range131942思考的步骤7.2 Search Insert Position.13294.3代码模板1657. 3 Search a 2D Matrix133第10章深度优先搜索173第8章暴力枚举法13510.1 Palindrome Partitioning1738.1 Subsets13510.2 Unique Paths1768.1.1递归1350.2.1深搜1768.1.2迭代.1371022备忘录法.1768.2 Subsets il13810.23动规177821递归13810.24数学公式1788.2.2迭代.14110.3 Unique Paths Il1798. 3 Permutations14210.3.1备忘录法1798.3.1 next permutation14210.3.2动规.180目录10.4 N-Queens1813.4 Maximal rectangle21310.5 N-Queens II18413.5 Best Time to Buy and Sell Stock10.6 Restore ip addresses186.21410.7 Combination Sum18813.6 Interleaving String21510.8 Combination Sum Il18913.7 Scramble String21710.9 Generate Parentheses.19013. 8 Minimum Path Sum.22210.10 Sudoku solver19213.9 Edit Distance22410.11 Word Search.19313. 10 Decode Ways.22610.12小结19513. 11 Distinct sub22710.12.1适用场景19513. 12 Word Break22810.122思考的步骤19513 13 Word Break il2300.12.3代码模板197第14章图23210.12.4深搜与回溯法的区別.19714. 1 Clone Graph23210.12.5深搜与递归的区别..197第15章细节实现题235第11章分治法19915.1 Reverse Integer2351.1 Pow(x, n)19915.2 Palindrome Number.23611. 2 Sqrt(x)20015.3 Insert Interval237第12章贪心法20115.4 Merge Intervals23812.1 Jump game20115.5 Minimum Window Substring23912.2 Jump game II15.6 Multiply Strings24112. 3 Best Time to buy and Sell stock 20415.7 Substring with Concatenation12. 4 Best Time to buy and sell stock l205of all words24412. 5 Longest Substring Without re15.8 Pascal,s Triangle245peating Characters20615.9 Pascals Triangle Il24612.6 Container with Most Water.. 207 15.10 Spiral Matrix24715.11 Spiral matrix II248第13章动态规划20915.12 ZigZag Conversion25013. 1 Triangle20915.13 Divide Two Integers25113.2 Maximum Subarray15. 14 Text Justification25313.3 Palindrome Partitioning II1215.15 Max Points on a line255目录第1章编程技巧在判断两个浮点数a和b是否相等时,不要用a==b,应该判断二者之差的绝对值fabs(a-b)是否小于某个阈值,例如1e-9。判断一个整数是否是为奇数,用x%2!=0,不要用x%2=1,因为ⅹ可能是负用char的值作为数组下标(例如,统计字符串中每个字符岀现的次数),要考虑到char可能是负数。有的人考虑到了,先强制转型为 unsigned int再用作下标,这仍然是错的。正确的做法是,先强制转型为 unsigned char,再用作下标。这涉及C十整型提升的规则,就不详述了。以下是关于STL使用技巧的,很多条款来自《 Effective STL》这本书。vector和 string优先于动态分配的数组首先,在性能上,由于 vector能够保证连续内存,因此一旦分配了后,它的性能跟原始数组相当;其次,如果用new,意味着你要确保后面进行孓 delete,一旦忘记了,就会出现BUG,且这样需要都写一行 delete,代码不够短再次,声明多维数组的话,只能一个一个new,例如int** ary = new int*[row_num];for(int i=0: i< row num; ++1)ary [i] new int [col_num]用 vector的话一行代码搞定vectorary(row_num, vector(col_num, 0))使用 reserve来避免不必要的重新分配第2章线性表这类题目考察线性表的操作,例如,数组,单链表,双向链表等。2数组2.1.1 Remove Duplicates from Sorted array描述Given a sorted array, remove the duplicates in place such that each element appear only onceand return the new lengthDo not allocate extra space for another array, you must do this in place with constant memoryFor example, Given input array A =[1, 1, 2Your function should return length =2, and a is now [1, 2]分析无代码1/ LeetCode, Remove Duplicates from Sorted Array//时间复杂度0(n),空间复杂度0(1)class Solution tublicint removeDuplicates(vector& nums)tif (nums empty o) return 0;int index =ofor (int i =1: i nums size: 1++ iif (nums [index] ! nums [i])nums [++index]= nums [i]return index 12.1数组代码2//LeetCode, Remove Duplicates from Sorted Array/使用STL,时间复杂度0(n),空间复杂度0(1)class Solution ipublicint removeDuplicates(vector& nums)treturn distance(nums begin(), unique(nums begin(), nums end ())代码3/ LeetCode, Remove Duplicates from Sorted Array/使用STL,时间复杂度0(n),空间复杂度0(1)lass Solution fublicint removeDuplicates(vector& nums)treturn distance(nums begin(, removeDuplicates(nums begin(, nums end(), nums begintemplateOutIt removeDuplicates(InIt first, InIt last, OutIt output)thile (first last)i*output++ = *firstfirst upper_bound(first, last, *firstreturn output相关题目Remove Duplicates from Sorted Array I,见§2.1.22.1.2 Remove Duplicates from Sorted Array II描述Follow up for"Remove Duplicates " What if duplicates are allowed at most twice?For example, Given sorted array a =[1, 1, 1, 2, 2, 3]Your function should return length=5, and A is now [1, 1, 2, 2, 3分析加一个变量记录一下元素出现的次数即可。这题因为是已经排序的数组,所以一个变量即可解决。如果是没有排序的数组,则需要引入一个 hashmap来记录出现次数。4第2章线性表代码1// Leet Code, Remove Duplicates from Sorted Array II//时间复杂度0(n),空间复杂度0(1)//qauthorhex108(https://github.com/hex108)class Solution tublicint removeDuplicates(vector& nums)tif (nums size(
- 2020-12-04下载
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Lectures on Stochastic Programming-Model
这是一本关于随机规划比较全面的书！比较难，不太容易啃，但是读了之后收获很大。这是高清版的！To Julia, Benjamin, Daniel, Nalan, and Yael;to Tsonka Konstatin and Marekand to the memory of feliks, Maria, and dentcho2009/8/20pagContentsList of notationserace1 Stochastic Programming ModelsIntroduction1.2 Invento1.2.1The news vendor problem1.2.2Constraints12.3Multistage modelsMultiproduct assembl1.3.1Two-Stage Model1.3.2Chance Constrained ModeMultistage modelPortfolio selection131.4.1Static model14.2Multistage Portfolio selection14.3Decision rule211.5 Supply Chain Network Design22Exercises2 Two-Stage Problems272.1 Linear Two-Stage Problems2.1.1Basic pi272.1.2The Expected Recourse Cost for Discrete Distributions 302.1.3The Expected Recourse Cost for General Distributions.. 322.1.4Optimality Conditions垂Polyhedral Two-Stage Problems422.2.1General Properties422.2.2Expected recourse CostOptimality conditions2.3 General Two-Stage Problems82.3.1Problem Formulation, Interchangeability482.3.2Convex Two-Stage Problems2.4 Nonanticipativity2009/8/20page villContents2.4.1Scenario formulation2.4.2Dualization of Nonanticipativity Constraints2.4.3Nonanticipativity duality for general Distributions2.4.4Value of perfect infExercises3 Multistage problems3. 1 Problem Formulation633.1.1The general setting3.1The Linear case653.1.3Scenario trees3.1.4Algebraic Formulation of nonanticipativity constraints 7lDuality....763.2.1Convex multistage problems·763.2.2Optimality Conditions3.2.3Dualization of Feasibility Constraints3.2.4Dualization of nonanticipativity ConstraintsExercises4 Optimization models with Probabilistic Constraints874.1 Introduction874.2 Convexity in Probabilistic Optimization4.2Generalized Concavity of Functions and measures4.2.2Convexity of probabilistically constrained sets1064.2.3Connectedness of Probabilistically Constrained Sets... 113Separable probabilistic Constraints.1144.3Continuity and Differentiability Properties ofDistribution functions4.3.2p-Efficient Points.1154.3.3Optimality Conditions and Duality Theory1224 Optimization Problems with Nonseparable Probabilistic Constraints.. 1324.4Differentiability of Probability Functions and OptimalityConditions13344.2Approximations of Nonseparable ProbabilisticConstraints134.5 Semi-infinite Probabilistic Problems144E1505 Statistical Inference155Statistical Properties of Sample Average Approximation Estimators.. 1555.1.1Consistency of SAA estimators1575.1.2Asymptotics of the saa Optimal value1635.1.3Second order asStochastic Programs5.2 Stoch1745.2.1Consistency of solutions of the SAA GeneralizedEquatio1752009/8/20pContents5.2.2Atotics of saa generalized equations estimators 1775.3 Monte Carlo Sampling Methods180Exponential Rates of Convergence and Sample sizeEstimates in the Case of a finite Feasible se1815.3.2Sample size estimates in the General Case1855.3.3Finite Exponential Convergence1915.4 Quasi-Monte Carlo Methods1935.Variance-Reduction Techniques198Latin hmpling1985.5.2Linear Control random variables method200ng and likelihood ratio methods 205.6 Validation analysis5.6.1Estimation of the optimality g2025.6.2Statistical Testing of Optimality Conditions2075.7Constrained Probler5.7.1Monte Carlo Sampling Approach2105.7.2Validation of an Optimal solution5.8 SAA Method Applied to Multistage Stochastic Programmin205.8.1Statistical Properties of Multistage SAA Estimators22l5.8.2Complexity estimates of Multistage Programs2265.9 Stochastic Approximation Method2305.9Classical Approach5.9.2Robust sA approach..23359.3Mirror Descent sa method235.9.4Accuracy Certificates for Mirror Descent Sa Solutions.. 244Exercis6 Risk Averse Optimi2536.1 Introductio6.2 Mean-Risk models.2546.2.1Main ideas of mean -Risk analysis546.2.2Semideviation6.2.3Weighted Mean Deviations from Quantiles.2566.2.4Average value-at-Risk2576.3 Coherent risk measures2616.3.1Differentiability Properties of Risk Measures2656.3.2Examples of risk Measures..2696.3.3Law invariant risk measures and Stochastic orders2796.3.4Relation to Ambiguous Chance Constraints2856.4 Optimization of risk measures.2886.4.1Dualization of Nonanticipativity Constraints2916.4.2Examples...2956.5 Statistical Properties of Risk measures6.5.IAverage value-at-Ris6.52Absolute semideviation risk measure301Von mises statistical functionals3046.6The problem of moments306中2009/8/20page xContents6.7 Multistage Risk Averse Optimization3086.7.1Scenario tree formulation3086.7.2Conditional risk mappings3156.7.3Risk Averse multistage Stochastic Programming318Exercises3287 Background material3337.1 Optimization and Convex Analysis..334Directional Differentiability3347.1.2Elements of Convex Analysis3367.1.3Optimization and duality3397.1.4Optimality Conditions.............3467.1.5Perturbation analysis3517.1.6Epiconvergence3572 Probability3597.2.1Probability spaces and random variables7.2.2Conditional Probability and Conditional Expectation... 36372.3Measurable multifunctions and random functions3657.2.4Expectation Functions.3687.2.5Uniform Laws of Large Numbers...,,3747.2.6Law of Large Numbers for Random Sets andSubdifferentials3797.2.7Delta method7.2.8Exponential Bounds of the Large Deviations Theory3877.2.9Uniform Exponential Bounds7.3 Elements of Functional analysis3997.3Conjugate duality and differentiability.......... 4017.3.2Lattice structure4034058 Bibliographical remarks407Biibliography415Index4312009/8/20pageList of Notationsequal by definition, 333IR", n-dimensional space, 333A, transpose of matrix(vector)A, 3336I, domain of the conjugate of risk mea-C(X) space of continuous functions, 165sure p, 262CK, polar of cone C, 337Cn, the space of nonempty compact sub-C(v,R"), space of continuously differ-sets of r 379entiable mappings,176set of probability density functions,I Fr influence function. 3042L, orthogonal of (linear) space L, 41Sz, set of contact points, 3990(1), generic constant, 188b(k; a, N), cdf of binomial distribution,Op(), term, 382214S, the set of &-optimal solutions of theo, distance generating function, 236true problem, 18g(x), right-hand-side derivative, 297Va(a), Lebesgue measure of set A C RdCl(A), topological closure of set A, 334195conv(C), convex hull of set C, 337W,(U), space of Lipschitz continuousCorr(X, Y), correlation of X and Y 200functions. 166. 353CoV(X, Y, covariance of X and y, 180[a]+=max{a,0},2ga, weighted mean deviation, 256IA(, indicator function of set A, 334Sc(, support function of set C, 337n(n.f. p). space. 399A(x), set ofdist(x, A), distance from point x to set Ae multipliers vectors334348dom f, domain of function f, 333N(μ,∑), nonmal distribution,16Nc, normal cone to set C, 337dom 9, domain of multifunction 9, 365IR, set of extended real numbers. 333o(z), cdf of standard normal distribution,epif, epigraph of function f, 333IIx, metric projection onto set X, 231epiconvergence, 377convergence in distribution, 163SN, the set of optimal solutions of the0(x,h)d order tangent set 348SAA problem. 156AVOR. Average value-at-Risk. 258Sa, the set of 8-optimal solutions of thef, set of probability measures, 306SAA problem. 181ID(A, B), deviation of set A from set Bn,N, optimal value of the Saa problem,334156IDIZ], dispersion measure of random vari-N(x), sample average function, 155able 7. 2541A(, characteristic function of set A, 334吧, expectation,361int(C), interior of set C, 336TH(A, B), Hausdorff distance between setsLa」, integer part of a∈R,219A and B. 334Isc f, lower semicontinuous hull of funcN, set of positive integers, 359tion f, 3332009/8/20pageList of notationsRc, radial cone to set C, 337C, tangent cone to set C, 337V-f(r), Hessian matrix of second orderpartial derivatives, 179a. subdifferential. 338a, Clarke generalized gradient, 336as, epsilon subdifferential, 380pos w, positive hull of matrix W, 29Pr(A), probability of event A, 360ri relative interior. 337upper semideviation, 255Le, lower semideviation, 255@R. Value-at-Risk. 25Var[X], variance of X, 149, optimal value of the true problem, 1565=(51,……,5), history of the process,{a,b},186r, conjugate of function/, 338f(x, d), generalized directional deriva-g(x, h), directional derivative, 334O,(, term, 382p-efficient point, 116lid, independently identically distributed,1562009/8/20page xlllPrefaceThe main topic of this book is optimization problems involving uncertain parametersfor which stochastic models are available. Although many ways have been proposed tomodel uncertain quantities stochastic models have proved their flexibility and usefulnessin diverse areas of science. This is mainly due to solid mathematical foundations andtheoretical richness of the theory of probabilitystochastic processes, and to soundstatistical techniques of using real dataOptimization problems involving stochastic models occur in almost all areas of scienceand engineering, from telecommunication and medicine to finance This stimulates interestin rigorous ways of formulating, analyzing, and solving such problems. Due to the presenceof random parameters in the model, the theory combines concepts of the optimization theory,the theory of probability and statistics, and functional analysis. Moreover, in recent years thetheory and methods of stochastic programming have undergone major advances. all thesefactors motivated us to present in an accessible and rigorous form contemporary models andideas of stochastic programming. We hope that the book will encourage other researchersto apply stochastic programming models and to undertake further studies of this fascinatinand rapidly developing areaWe do not try to provide a comprehensive presentation of all aspects of stochasticprogramming, but we rather concentrate on theoretical foundations and recent advances inselected areas. The book is organized into seven chapters The first chapter addresses modeling issues. The basic concepts, such as recourse actions, chance(probabilistic)constraintsand the nonanticipativity principle, are introduced in the context of specific models. Thediscussion is aimed at providing motivation for the theoretical developments in the book,rather than practical recommendationsChapters 2 and 3 present detailed development of the theory of two-stage and multistage stochastic programming problems. We analyze properties of the models and developoptimality conditions and duality theory in a rather general setting. Our analysis coversgeneral distributions of uncertain parameters and provides special results for discrete distributions, which are relevant for numerical methods. Due to specific properties of two- andmultistage stochastic programming problems, we were able to derive many of these resultswithout resorting to methods of functional analvsisThe basic assumption in the modeling and technical developments is that the proba-bility distribution of the random data is not influenced by our actions(decisions). In someapplications, this assumption could be unjustified. However, dependence of probability dis-tribution on decisions typically destroys the convex structure of the optimization problemsconsidered, and our analysis exploits convexity in a significant way
- 2020-12-09下载
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