diff --git a/opt/smm/solver.py b/opt/smm/solver.py
new file mode 100644
index 0000000..5635985
--- /dev/null
+++ b/opt/smm/solver.py
@@ -0,0 +1,518 @@
+# -*- coding: gb2312 -*-
+import copy
+
+import numpy as np
+import time
+from collections import defaultdict, deque
+
+
+class GRB:
+    CONTINUOUS = 'C'  # 连续变量
+    INTEGER = 'I'  # 整数变量
+    BINARY = 'B'  # 二元变量 (0-1)
+
+    OPTIMAL = 0
+    UNBOUNDED = 1
+    INFEASIBLE = 2
+    TIME_LIMIT = 3
+    INTERRUPTED = 4
+
+    MINIMIZE = 0
+    MAXIMIZE = 1
+
+
+class Simplex:
+    def __init__(self, obj, max_mode=False):  # default is solve min LP, if want to solve max lp,should * -1
+        self.mat, self.max_mode = np.array([[0] + obj]) * (-1 if max_mode else 1), max_mode
+        self.res = None
+        self.objVal = 0
+        self.varVal = None
+
+    def add_constraint(self, a, b):
+        self.mat = np.vstack([self.mat, [b] + a])
+
+    def _simplex(self, mat, B, m, n):
+        while mat[0, 1:].min() < -1e-10:
+            col = np.where(mat[0, 1:] < 0)[0][0] + 1  # use Bland's method to avoid degeneracy.
+            row = np.array([mat[i][0] / mat[i][col] if mat[i][col] > 0 else 0x7fffffff for i in
+                            range(1, mat.shape[0])]).argmin() + 1  # find the theta index
+            if mat[row][col] <= -1e-10:
+                self.res = GRB.UNBOUNDED
+                return None  # the theta is ∞, the problem is unbounded
+            self._pivot(mat, B, row, col)
+        self.res = GRB.OPTIMAL
+        return mat[0][0] * (1 if self.max_mode else -1), {B[i]: mat[i, 0] for i in range(1, m) if B[i] < n}
+
+    def _pivot(self, mat, B, row, col):
+        mat[row] /= mat[row][col]
+        ids = np.arange(mat.shape[0]) != row
+        mat[ids] -= mat[row] * mat[ids, col:col + 1]  # for each i!= row do: mat[i]= mat[i] - mat[row] * mat[i][col]
+        B[row] = col
+
+    def solve(self):
+        m, n = self.mat.shape  # m - 1 is the number slack variables we should add
+        temp, B = np.vstack([np.zeros((1, m - 1)), np.eye(m - 1)]), list(range(n - 1, n + m - 1))  # add diagonal array
+        mat = np.hstack([self.mat, temp])  # combine them!
+        if mat[1:, 0].min() < -1e-10:  # is the initial basic solution feasible?
+            row = mat[1:, 0].argmin() + 1  # find the index of min b
+            temp, mat[0] = np.copy(mat[0]), 0  # set first row value to zero, and store the previous value
+            mat = np.hstack([mat, np.array([1] + [-1] * (m - 1)).reshape((-1, 1))])
+            self._pivot(mat, B, row, mat.shape[1] - 1)
+            try:
+                if self._simplex(mat, B, m, n)[0] != 0:
+                    self.res = GRB.INFEASIBLE
+                    return None  # the problem has no answer
+            except:
+                return None # the problem is unbounded
+
+            if mat.shape[1] - 1 in B:  # if the x0 in B, we should pivot it.
+                self._pivot(mat, B, B.index(mat.shape[1] - 1), np.where(mat[0, 1:] != 0)[0][0] + 1)
+            mat = np.vstack([temp, mat[1:, :-1]])  # recover the first line
+            for i, x in enumerate(B[1:]):
+                mat[0] -= mat[0, x] * mat[i + 1]
+        self._simplex(mat, B, m, n)
+
+        if self.res == GRB.OPTIMAL:
+            self.objVal = mat[0][0] * (1 if self.max_mode else -1)
+            self.varVal = {B[i]: mat[i, 0] for i in range(1, m) if B[i] < n}
+
+
+class LinearExpr:
+    def __init__(self, constant=0, coefficients=None):
+        self.constant = constant  # 常数项
+        self.coefficients = coefficients or {}  # 变量名到系数的映射
+
+    @classmethod
+    def from_var(cls, var, coeff=1):
+        return cls(0, {var.name: coeff})
+
+    @classmethod
+    def from_constant(cls, value):
+        """从常数创建线性表达式"""
+        return cls(value, {})
+
+    def copy(self):
+        """创建副本"""
+        return LinearExpr(self.constant, self.coefficients.copy())
+
+    def get_coeff(self, var_name):
+        """直接获取变量的系数，无需递归"""
+        return self.coefficients.get(var_name, 0)
+
+    def set_coeff(self, var_name, coeff):
+        """直接设置变量的系数"""
+        if coeff == 0 and var_name in self.coefficients:
+            del self.coefficients[var_name]
+        elif coeff != 0:
+            self.coefficients[var_name] = coeff
+
+    def add_coeff(self, var_name, coeff):
+        """增加变量的系数"""
+        current = self.coefficients.get(var_name, 0)
+        new_coeff = current + coeff
+        self.set_coeff(var_name, new_coeff)
+
+    def __add__(self, other):
+        result = self.copy()
+
+        if isinstance(other, LinearExpr):
+            result.constant += other.constant
+            for var_name, coeff in other.coefficients.items():
+                result.add_coeff(var_name, coeff)
+        elif isinstance(other, Var):
+            result.add_coeff(other.name, 1)
+        else:  # 常数
+            result.constant += other
+
+        return result
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __sub__(self, other):
+        return self + (-1 * other)
+
+    def __rsub__(self, other):
+        return (-1 * self) + other
+
+    def __mul__(self, other):
+        if not isinstance(other, (int, float)):
+            raise ValueError("linear expressions can only be multiplied by scalars.")
+
+        result = LinearExpr(self.constant * other)
+        for var_name, coeff in self.coefficients.items():
+            result.coefficients[var_name] = coeff * other
+
+        return result
+
+    def __rmul__(self, other):
+        return self.__mul__(other)
+
+    def __neg__(self):
+        return -1 * self
+
+    def __le__(self, other):
+        return Constraint(self, other, '<=')
+
+    def __ge__(self, other):
+        return Constraint(self, other, '>=')
+
+    def __eq__(self, other):
+        return Constraint(self, other, '==')
+
+
+class Var:
+    def __init__(self, name, vtype, lb=None, ub=None):
+        self.name = name
+        self.lb = lb
+        self.ub = ub
+        self.idx = 0
+        self.x = 0
+        self.vtype = vtype
+        self.Start = None   # todo: start solution
+
+    def to_expr(self):
+        return LinearExpr.from_var(self)
+
+    def __str__(self):
+        return self.name
+
+    # 委托给线性表达式的方法
+    def __add__(self, other):
+        return self.to_expr() + other
+
+    def __radd__(self, other):
+        return other + self.to_expr()
+
+    def __sub__(self, other):
+        return self.to_expr() - other
+
+    def __rsub__(self, other):
+        return other - self.to_expr()
+
+    def __mul__(self, other):
+        return self.to_expr() * other
+
+    def __rmul__(self, other):
+        return other * self.to_expr()
+
+    def __le__(self, other):
+        return self.to_expr() <= other
+
+    def __ge__(self, other):
+        return self.to_expr() >= other
+
+    def __eq__(self, other):
+        return self.to_expr() == other
+
+    def __neg__(self):
+        return LinearExpr.from_var(self, -1)
+
+
+class Constraint:
+    def __init__(self, lhs, rhs, sense):
+        self.lhs = self._to_linear_expr(lhs)
+        self.rhs = self._to_linear_expr(rhs)
+        self.sense = sense  # '<=', '>=', '=='
+
+    @staticmethod
+    def _to_linear_expr(expr):
+        if isinstance(expr, LinearExpr):
+            return expr
+        elif isinstance(expr, Var):
+            return expr.to_expr()
+        elif isinstance(expr, (int, float)):
+            return LinearExpr.from_constant(expr)
+        else:
+            raise TypeError(f"unsupported expression type: {type(expr)}")
+
+
+# @Date    : 2025/11/10
+# @Author  : guangyu-lu
+class Model:
+    def __init__(self, name=""):
+        self.name = name
+        self.variables = {}
+        self.constraints = []
+        self.NameNumVars = defaultdict(int)
+        self.NumVars = 0
+
+        self.Status = None
+        self.objval = 0
+        self.TimeLimit = np.inf
+        self.OutputFlag = True
+        self.modelSense = None
+
+    def terminate(self):
+        pass        # todo: terminate function
+
+    def addVar(self, vtype=GRB.CONTINUOUS, lb=0, ub=np.inf, name=None):
+        if name:
+            if cnt := self.NameNumVars[name]:
+                var_name = f"{name}_{cnt}"
+            else:
+                self.NameNumVars.pop(name)
+                self.NameNumVars[name] += 1
+                var_name = name
+        else:
+            cnt = self.NameNumVars["usr_name"]
+            var_name = f"usr_name_{cnt}"
+            self.NameNumVars["usr_name"] += 1
+
+        variable = Var(var_name, vtype)
+        variable.idx = self.NumVars
+        variable.lb = lb
+        variable.ub = ub
+        self.variables[var_name] = variable
+        self.NumVars += 1
+        return variable
+
+    def addVars(self, *indices, vtype, lb=0, ub=np.inf, name=None):
+        # todo: negative variable(s) solving
+        result = defaultdict(Var)
+        usr_name = "usr_name" if name is None else name
+
+        from itertools import product
+        index_ranges = [range(dim) if isinstance(dim, int) else dim for dim in indices]
+        for index_tuple in product(*index_ranges):
+            var_name = f"{name}_{self.NameNumVars[usr_name]}"
+            self.NameNumVars[usr_name] += 1
+
+            var = self.addVar(name=var_name, lb=lb, ub=ub, vtype=vtype)
+            result[index_tuple if len(index_tuple) > 1 else index_tuple[0]] = var
+
+        return result
+
+    def addConstr(self, constraint):
+        """添加约束"""
+        self.constraints.append(constraint)
+        return constraint
+
+    def addConstrs(self, constraints):
+        for constraint in constraints:
+            self.addConstr(constraint)
+
+    def setObjective(self, expr, sense=GRB.MINIMIZE):
+        """设置目标函数"""
+        self.objective = Constraint(expr, 0, '==').lhs
+        self.modelSense = sense
+
+    def optimize(self):
+        # todo: set start solution
+        obj = [0] * len(self.variables)
+        for var_, coeff in self.objective.coefficients.items():
+            obj[self.variables[var_].idx] = coeff
+
+        sv = Simplex(obj, max_mode=self.modelSense == GRB.MAXIMIZE)
+        for constr in self.constraints:
+            constr_expr = [0] * len(self.variables)
+            for var_, coeff in constr.lhs.coefficients.items():
+                constr_expr[self.variables[var_].idx] += coeff
+
+            for var_, coeff in constr.rhs.coefficients.items():
+                constr_expr[self.variables[var_].idx] -= coeff
+
+            if constr.sense == '<=':
+                sv.add_constraint(constr_expr, constr.rhs.constant - constr.lhs.constant + 1e-10)
+            elif constr.sense == ">=":
+                sv.add_constraint([-c for c in constr_expr], constr.lhs.constant - constr.rhs.constant + 1e-10)
+            else:  # '=='
+                sv.add_constraint(constr_expr, constr.rhs.constant - constr.lhs.constant + 1e-10)
+                sv.add_constraint([-c for c in constr_expr], constr.lhs.constant - constr.rhs.constant + 1e-10)
+
+        # for variable bound
+        for var in self.variables.values():
+            if var.vtype == GRB.BINARY:
+                constr_expr = [0] * len(self.variables)
+                constr_expr[var.idx] = 1
+                sv.add_constraint(constr_expr, 1)
+            else:
+                if var.ub != np.inf:
+                    constr_expr = [0] * len(self.variables)
+                    constr_expr[var.idx] = 1
+                    sv.add_constraint(constr_expr, var.ub + 1e-10)
+                if var.lb != 0:
+                    constr_expr = [0] * len(self.variables)
+                    constr_expr[var.idx] = -1
+                    sv.add_constraint(constr_expr, -var.lb + 1e-10)
+
+        class Node:
+            def __init__(self, solver, lb=0, ub=np.inf):
+                self.sv = solver
+                self.lb = lb
+                self.ub = ub
+
+                self.idx_col = {i: i + 1 for i in range(self.sv.mat.shape[1] - 1)}
+                self.fixed_var = dict()
+
+        start_time = time.time()
+        sv.solve()
+        if sv.res != GRB.OPTIMAL:
+            self.Status = sv.res
+            return
+
+        sense = 1 if sv.max_mode else -1
+        global_ub, global_lower = sv.objVal * sense, -np.inf
+        queue = deque()
+        root_node = Node(sv, 0, global_ub)
+        incub_node, cur_node = None, root_node
+
+        # todo: tightening constraint(s)
+        # todo: pre-solve remove row(s) and column(s)
+        while True:
+            if self.OutputFlag:
+                pass    # todo: show the solving process
+            if time.time() - start_time > self.TimeLimit:
+                self.Status = GRB.TIME_LIMIT
+                break
+            if cur_node.sv.res == GRB.OPTIMAL:
+                if cur_node.sv.objVal * sense > global_ub + 1e-10:
+                    continue    # node prune
+                branch_idx = -1
+
+                for var in self.variables.values():
+                    if var.idx not in cur_node.idx_col.keys():
+                        continue
+
+                    if (col := cur_node.idx_col[var.idx]) not in cur_node.sv.varVal.keys():
+                        continue
+
+                    val = cur_node.sv.varVal[col]
+                    if var.vtype == GRB.INTEGER and abs(round(val) - val) > 1e-8:
+                        branch_idx = var.idx
+
+                        if cur_node.ub >= global_ub:
+                            left_constr_expr = [0] * (cur_node.sv.mat.shape[1] - 1)
+                            left_constr_expr[col - 1] = 1
+
+                            left_node = copy.deepcopy(cur_node)
+                            left_node.sv.add_constraint(left_constr_expr, int(val) + 1e-10)
+
+                            queue.append(left_node)
+
+                            right_constr_expr = [0] * (cur_node.sv.mat.shape[1] - 1)
+                            right_constr_expr[col - 1] = -1
+
+                            right_node = copy.deepcopy(cur_node)
+                            right_node.sv.add_constraint(right_constr_expr, -int(val) - 1 + 1e-10)
+
+                            queue.append(right_node)
+                        break
+
+                    elif var.vtype == GRB.BINARY and abs(val) > 1e-8 and abs(val - 1) > 1e-8:
+                        branch_idx = var.idx
+                        # todo: remove columns corresponding to the sum of binary variables
+                        # remove the variable corresponding columns
+                        for idx in cur_node.idx_col.keys():
+                            if idx > var.idx:
+                                cur_node.idx_col[idx] -= 1
+                        cur_node.idx_col.pop(var.idx)
+
+                        left_node = copy.deepcopy(cur_node)
+                        left_node.sv.mat = np.delete(left_node.sv.mat, col, axis=1)
+                        left_node.fixed_var[var.idx] = 0
+                        queue.append(left_node)
+
+                        right_node = copy.deepcopy(cur_node)
+                        for row in range(right_node.sv.mat.shape[0]):
+                            right_node.sv.mat[row][0] -= right_node.sv.mat[row][col]
+                        right_node.sv.mat = np.delete(right_node.sv.mat, col, axis=1)
+                        right_node.fixed_var[var.idx] = 1
+                        queue.append(right_node)
+                        break
+
+                if branch_idx == -1:
+                    cur_node.lb = cur_node.ub = cur_node.sv.objVal * sense
+                    if cur_node.lb > global_lower:
+                        # todo: solution pool
+                        global_lower = cur_node.lb
+                        incub_node = cur_node
+
+            # todo: stop optimizer and output the incumbent result
+            if len(queue) == 0 or global_ub - global_lower <= 1e-8:
+                break
+
+            cur_node = queue.popleft()
+            cur_node.sv.solve()         # todo: dual simplex / interior algorithm
+            # todo: primal heuristic - esp. rounding and pumping
+            # todo: cutting plane :
+            #  Gomory*, Cover, Implied bound, Projected implied bound, MIR, Flow cover, Zero half, RLT, Relax-and-lift BQP
+
+        if incub_node and incub_node.sv.res == GRB.OPTIMAL:
+            self.objval = round(incub_node.sv.objVal * 1e8) / 1e8 + self.objective.constant
+
+            # convert the variable result
+            idx2var = {var.idx: var.name for var in self.variables.values()}
+            for idx, val in incub_node.fixed_var.items():
+                self.variables[idx2var[idx]].x = val
+
+            col2idx = {col: idx for idx, col in incub_node.idx_col.items()}
+            for col, val in incub_node.sv.varVal.items():
+                idx = col2idx[int(col)]
+                if self.variables[idx2var[idx]].vtype == GRB.INTEGER or self.variables[idx2var[idx]].vtype == GRB.BINARY:
+                    self.variables[idx2var[idx]].x = round(val)
+                else:
+                    self.variables[idx2var[idx]].x = round(val * 1e8) / 1e8
+
+        if incub_node and self.Status != GRB.TIMEOUT:
+            self.Status = GRB.OPTIMAL
+        else:
+            self.Status = GRB.INFEASIBLE
+
+
+def quicksum(expr_list):
+    if not expr_list:
+        return LinearExpr.from_constant(0)
+
+    constant = 0
+    coefficients = {}
+
+    for expr in expr_list:
+        if isinstance(expr, LinearExpr):
+            linear_expr = expr
+        elif isinstance(expr, Var):
+            linear_expr = expr.to_expr()
+        elif isinstance(expr, (int, float)):
+            linear_expr = LinearExpr.from_constant(expr)
+        else:
+            raise TypeError(f"Unsupported Expression Type: {type(expr)}")
+
+        # 累加常数项
+        constant += linear_expr.constant
+
+        # 累加系数
+        for var_name, coeff in linear_expr.coefficients.items():
+            if var_name in coefficients:
+                coefficients[var_name] += coeff
+            else:
+                coefficients[var_name] = coeff
+
+    return LinearExpr(constant, coefficients)
+
+
+# 测试代码
+if __name__ == "__main__":
+    # todo: test instance
+    mdl = Model()
+
+    x = mdl.addVars(4, ub=10, vtype=GRB.CONTINUOUS, name='x')
+
+    # == Objective function ===
+    mdl.setObjective(3 * x[0] + 9 * x[1] + 20 * x[2] + 19 * x[3], GRB.MINIMIZE)
+
+    # === Constraint ===
+    mdl.addConstr(110 * x[0] + 160 * x[1] + 420 * x[2] + 260 * x[3] >= 2000)
+    mdl.addConstr(4 * x[0] + 8 * x[1] + 4 * x[2] + 14 * x[3] >= 55)
+    mdl.addConstr(2 * x[0] + 285 * x[1] + 22 * x[2] + 80 * x[3] >= 800)
+
+    mdl.optimize()      # todo: callback function
+    if mdl.Status == GRB.OPTIMAL:
+        print(f"objective = {mdl.objval}")
+        for name, var in mdl.variables.items():
+            print(f"{name} = {var.x}")
+    elif mdl.Status == GRB.INFEASIBLE:
+        print('infeasible model')
+    elif mdl.Status == GRB.UNBOUNDED:
+        print('unbounded model')
+
+