Slowest and fastest coupon collectors

In the coupon collector's problem, every cereal box contains one coupon from a collection of $n$ distinct coupons, each equally likely to appear. The goal is to find the expected number of boxes a player needs to purchase to complete the whole collection. In this work, we extend the classical problem to $k$ players, and find the expected number of boxes required for the slowest and fastest players to complete the whole collection. The probability that a particular player is the slowest or fastest player to finish will also be touched upon

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization

We focus on kernel methods for set-valued inputs and their application to Bayesian set optimization, notably combinatorial optimization. We investigate two classes of set kernels that both rely on Reproducing Kernel Hilbert Space embeddings, namely the ``Double Sum'' (DS) kernels recently considered in Bayesian set optimization, and a class introduced here called ``Deep Embedding'' (DE) kernels that essentially consists in applying a radial kernel on Hilbert space on top of the canonical distance induced by another kernel such as a DS kernel. We establish in particular that while DS kernels typically suffer from a lack of strict positive definiteness, vast subclasses of DE kernels built upon DS kernels do possess this property, enabling in turn combinatorial optimization without requiring to introduce a jitter parameter. Proofs of theoretical results about considered kernels are complemented by a few practicalities regarding hyperparameter fitting. We furthermore demonstrate the applicability of our approach in prediction and optimization tasks, relying both on toy examples and on two test cases from mechanical engineering and hydrogeology, respectively. Experimental results highlight the applicability and compared merits of the considered approaches while opening new perspectives in prediction and sequential design with set inputs

No-feedback Card Guessing Game: Moments and distributions under the optimal strategy

Relying on the optimal guessing strategy recently found for a no-feedback card guessing game with $k$-time riffle shuffles, we derive an exact, closed-form formula for the expected number of correct guesses and higher moments for a $1$-time shuffle case. Our approach makes use of the fast generating function based on a recurrence relation, the method of overlapping stages, and interpolation. As for $k>1$-time shuffles, we establish the expected number of correct guesses through a self-contained combinatorial proof. The proof turns out to be the answer to an open problem listed in Krityakierne and Thanatipanonda (2022), asking for a combinatorial interpretation of a generating function object introduced therein

Global Optimization Of Computationally Expensive Blackbox Problems Using Radial Basis Functions

Three derivative-free global optimization methods are developed based on radial basis functions (RBFs) for computationally expensive blackbox simulation models. First, we develop a multistart global optimization method, called SOMS (SurrOgate MultiStart). SOMS uses an RBF surrogate model to approximate the objective function in order to reduce the number of function evaluations necessary to identify the most promising points from which each nonlinear programming local search is started. We show that SOMS detects any local minimum within a finite number of iterations almost surely. The numerical results show that SOMS performs favorably in comparison to alternative methods and that the surrogate approach saves a significant number of computationally expensive function evaluations. In the second part of this work, we introduce PADS (PArallel Dynamic coordinate search with Surrogates), which is a surrogate-based global optimization framework for highdimensional expensive blackbox functions. In each parallel iteration of PADS, multiple points are selected from a large set of candidate points that are generated by perturbing only a subset of the coordinates of the current best solution. The selected points are then evaluated in parallel with up to 16 parallel processors. We show that PADS converges to the global optimum with probability 1. We develop two versions, PADS1 and PADS2, which use different underlying distributions to generate candidate points. We show that PADS1 and PADS2 are able to find better solutions more efficiently compared to alternative methods, with PADS1 performing even better than PADS2 in problems up to 200 dimensions. In the final part of this dissertation, we develop an effective new parallel surrogate global optimization method called SOP (Surrogate Optimization with Pareto center selection). The search mechanism of SOP incorporates bi-objective optimization, tabu search, and surrogate assisted local search, which exploits the information from the already evaluated points, for selecting a large number of new evaluation points. The newly selected points are evaluated in parallel, and hence a significant reduction in wall-clock time can be achieved. We give sufficient conditions for almost sure convergence of SOP. The results of our numerical experiments show that SOP performs very well compared to alternative parallel surrogate model algorithms with 8 and 32 processors obtaining superlinear speedup on some test problems

SOMS: SurrOgate MultiStart algorithm for use with nonlinear programming for global optimization

SOMS is a general surrogate-based multistart algorithm, which is used in combination with any local optimizer to find global optima for computationally expensive functions with multiple local minima. SOMS differs from previous multistart methods in that a surrogate approximation is used by the multistart algorithm to help reduce the number of function evaluations necessary to identify the most promising points from which to start each nonlinear programming local search. SOMS’s numerical results are compared with four well-known methods, namely, Multi-Level Single Linkage (MLSL), MATLAB’s MultiStart, MATLAB’s GlobalSearch, and GLOBAL. In addition, we propose a class of wavy test functions that mimic the wavy nature of objective functions arising in many black-box simulations. Extensive comparisons of algorithms on the wavy testfunctions and on earlier standard global-optimization test functions are done for a total of 19 different test problems. The numerical results indicate that SOMS performs favorably in comparison to alternative methods and does especially well on wavy functions when the number of function evaluations allowed is limited

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization

We focus on kernel methods for set-valued inputs and their application to Bayesian set optimization, notably combinatorial optimization. We investigate two classes of set kernels that both rely on Reproducing Kernel Hilbert Space embeddings, namely the "Double Sum" (DS) kernels recently considered in Bayesian set optimization, and a class introduced here called "Deep Embedding" (DE) kernels that essentially consists in applying a radial kernel on Hilbert space on top of the canonical distance induced by another kernel such as a DS kernel. We establish in particular that while DS kernels typically suffer from a lack of strict positive definiteness, vast subclasses of DE kernels built upon DS kernels do possess this property, enabling in turn combinatorial optimization without requiring to introduce a jitter parameter. Proofs of theoretical results about considered kernels are complemented by a few practicalities regarding hyperparameter fitting. We furthermore demonstrate the applicability of our approach in prediction and optimization tasks, relying both on toy examples and on two test cases from mechanical engineering and hydrogeology, respectively. Experimental results highlight the applicability and compared merits of the considered approaches while opening new perspectives in prediction and sequential design with set inputs

SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors